From f6333da7ffb105d1fe95345788cf11176d4ce3e1 Mon Sep 17 00:00:00 2001
From: enkilee <jeffrey0122@163.com>
Date: Tue, 12 Aug 2025 14:57:27 +0800
Subject: [PATCH] swin_b

---
 samples/torchvision/swin_b/graph_hash.txt     |     1 +
 samples/torchvision/swin_b/graph_net.json     |     6 +
 samples/torchvision/swin_b/input_meta.py      |     0
 .../swin_b/input_tensor_constraints.py        |     0
 samples/torchvision/swin_b/model.py           |  4774 ++
 samples/torchvision/swin_b/weight_meta.py     | 70796 ++++++++++++++++
 6 files changed, 75577 insertions(+)
 create mode 100644 samples/torchvision/swin_b/graph_hash.txt
 create mode 100644 samples/torchvision/swin_b/graph_net.json
 create mode 100644 samples/torchvision/swin_b/input_meta.py
 create mode 100644 samples/torchvision/swin_b/input_tensor_constraints.py
 create mode 100644 samples/torchvision/swin_b/model.py
 create mode 100644 samples/torchvision/swin_b/weight_meta.py

diff --git a/samples/torchvision/swin_b/graph_hash.txt b/samples/torchvision/swin_b/graph_hash.txt
new file mode 100644
index 00000000..da5f28ad
--- /dev/null
+++ b/samples/torchvision/swin_b/graph_hash.txt
@@ -0,0 +1 @@
+4ee23de8fc53a1a21e37103fb6d58ae3c2043bb8d79f10407e7127bfacd2f4e4
\ No newline at end of file
diff --git a/samples/torchvision/swin_b/graph_net.json b/samples/torchvision/swin_b/graph_net.json
new file mode 100644
index 00000000..b6ffe9f7
--- /dev/null
+++ b/samples/torchvision/swin_b/graph_net.json
@@ -0,0 +1,6 @@
+{
+    "framework": "torch",
+    "num_devices_required": 1,
+    "num_nodes_required": 1,
+    "dynamic": false
+}
\ No newline at end of file
diff --git a/samples/torchvision/swin_b/input_meta.py b/samples/torchvision/swin_b/input_meta.py
new file mode 100644
index 00000000..e69de29b
diff --git a/samples/torchvision/swin_b/input_tensor_constraints.py b/samples/torchvision/swin_b/input_tensor_constraints.py
new file mode 100644
index 00000000..e69de29b
diff --git a/samples/torchvision/swin_b/model.py b/samples/torchvision/swin_b/model.py
new file mode 100644
index 00000000..fc64be2c
--- /dev/null
+++ b/samples/torchvision/swin_b/model.py
@@ -0,0 +1,4774 @@
+import torch
+
+
+class GraphModule(torch.nn.Module):
+    def forward(
+        self,
+        L_self_modules_features_modules_0_modules_0_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_0_modules_0_parameters_bias_: torch.nn.parameter.Parameter,
+        L_x_: torch.Tensor,
+        L_self_modules_features_modules_0_modules_2_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_0_modules_2_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_1_modules_0_modules_norm1_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_1_modules_0_modules_norm1_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_1_modules_0_modules_attn_parameters_relative_position_bias_table_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_1_modules_0_modules_attn_buffers_relative_position_index_: torch.Tensor,
+        L_self_modules_features_modules_1_modules_0_modules_attn_modules_qkv_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_1_modules_0_modules_attn_modules_proj_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_1_modules_0_modules_attn_modules_qkv_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_1_modules_0_modules_attn_modules_proj_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_1_modules_0_modules_norm2_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_1_modules_0_modules_norm2_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_1_modules_0_modules_mlp_modules_0_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_1_modules_0_modules_mlp_modules_0_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_1_modules_0_modules_mlp_modules_3_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_1_modules_0_modules_mlp_modules_3_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_1_modules_1_modules_norm1_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_1_modules_1_modules_norm1_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_1_modules_1_modules_attn_parameters_relative_position_bias_table_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_1_modules_1_modules_attn_buffers_relative_position_index_: torch.Tensor,
+        L_self_modules_features_modules_1_modules_1_modules_attn_modules_qkv_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_1_modules_1_modules_attn_modules_proj_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_1_modules_1_modules_attn_modules_qkv_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_1_modules_1_modules_attn_modules_proj_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_1_modules_1_modules_norm2_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_1_modules_1_modules_norm2_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_1_modules_1_modules_mlp_modules_0_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_1_modules_1_modules_mlp_modules_0_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_1_modules_1_modules_mlp_modules_3_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_1_modules_1_modules_mlp_modules_3_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_2_modules_norm_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_2_modules_norm_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_2_modules_reduction_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_3_modules_0_modules_norm1_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_3_modules_0_modules_norm1_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_3_modules_0_modules_attn_parameters_relative_position_bias_table_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_3_modules_0_modules_attn_buffers_relative_position_index_: torch.Tensor,
+        L_self_modules_features_modules_3_modules_0_modules_attn_modules_qkv_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_3_modules_0_modules_attn_modules_proj_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_3_modules_0_modules_attn_modules_qkv_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_3_modules_0_modules_attn_modules_proj_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_3_modules_0_modules_norm2_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_3_modules_0_modules_norm2_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_3_modules_0_modules_mlp_modules_0_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_3_modules_0_modules_mlp_modules_0_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_3_modules_0_modules_mlp_modules_3_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_3_modules_0_modules_mlp_modules_3_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_3_modules_1_modules_norm1_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_3_modules_1_modules_norm1_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_3_modules_1_modules_attn_parameters_relative_position_bias_table_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_3_modules_1_modules_attn_buffers_relative_position_index_: torch.Tensor,
+        L_self_modules_features_modules_3_modules_1_modules_attn_modules_qkv_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_3_modules_1_modules_attn_modules_proj_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_3_modules_1_modules_attn_modules_qkv_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_3_modules_1_modules_attn_modules_proj_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_3_modules_1_modules_norm2_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_3_modules_1_modules_norm2_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_3_modules_1_modules_mlp_modules_0_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_3_modules_1_modules_mlp_modules_0_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_3_modules_1_modules_mlp_modules_3_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_3_modules_1_modules_mlp_modules_3_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_4_modules_norm_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_4_modules_norm_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_4_modules_reduction_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_0_modules_norm1_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_0_modules_norm1_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_0_modules_attn_parameters_relative_position_bias_table_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_0_modules_attn_buffers_relative_position_index_: torch.Tensor,
+        L_self_modules_features_modules_5_modules_0_modules_attn_modules_qkv_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_0_modules_attn_modules_proj_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_0_modules_attn_modules_qkv_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_0_modules_attn_modules_proj_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_0_modules_norm2_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_0_modules_norm2_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_0_modules_mlp_modules_0_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_0_modules_mlp_modules_0_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_0_modules_mlp_modules_3_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_0_modules_mlp_modules_3_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_1_modules_norm1_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_1_modules_norm1_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_1_modules_attn_parameters_relative_position_bias_table_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_1_modules_attn_buffers_relative_position_index_: torch.Tensor,
+        L_self_modules_features_modules_5_modules_1_modules_attn_modules_qkv_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_1_modules_attn_modules_proj_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_1_modules_attn_modules_qkv_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_1_modules_attn_modules_proj_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_1_modules_norm2_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_1_modules_norm2_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_1_modules_mlp_modules_0_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_1_modules_mlp_modules_0_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_1_modules_mlp_modules_3_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_1_modules_mlp_modules_3_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_2_modules_norm1_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_2_modules_norm1_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_2_modules_attn_parameters_relative_position_bias_table_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_2_modules_attn_buffers_relative_position_index_: torch.Tensor,
+        L_self_modules_features_modules_5_modules_2_modules_attn_modules_qkv_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_2_modules_attn_modules_proj_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_2_modules_attn_modules_qkv_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_2_modules_attn_modules_proj_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_2_modules_norm2_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_2_modules_norm2_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_2_modules_mlp_modules_0_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_2_modules_mlp_modules_0_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_2_modules_mlp_modules_3_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_2_modules_mlp_modules_3_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_3_modules_norm1_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_3_modules_norm1_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_3_modules_attn_parameters_relative_position_bias_table_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_3_modules_attn_buffers_relative_position_index_: torch.Tensor,
+        L_self_modules_features_modules_5_modules_3_modules_attn_modules_qkv_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_3_modules_attn_modules_proj_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_3_modules_attn_modules_qkv_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_3_modules_attn_modules_proj_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_3_modules_norm2_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_3_modules_norm2_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_3_modules_mlp_modules_0_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_3_modules_mlp_modules_0_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_3_modules_mlp_modules_3_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_3_modules_mlp_modules_3_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_4_modules_norm1_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_4_modules_norm1_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_4_modules_attn_parameters_relative_position_bias_table_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_4_modules_attn_buffers_relative_position_index_: torch.Tensor,
+        L_self_modules_features_modules_5_modules_4_modules_attn_modules_qkv_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_4_modules_attn_modules_proj_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_4_modules_attn_modules_qkv_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_4_modules_attn_modules_proj_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_4_modules_norm2_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_4_modules_norm2_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_4_modules_mlp_modules_0_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_4_modules_mlp_modules_0_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_4_modules_mlp_modules_3_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_4_modules_mlp_modules_3_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_5_modules_norm1_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_5_modules_norm1_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_5_modules_attn_parameters_relative_position_bias_table_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_5_modules_attn_buffers_relative_position_index_: torch.Tensor,
+        L_self_modules_features_modules_5_modules_5_modules_attn_modules_qkv_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_5_modules_attn_modules_proj_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_5_modules_attn_modules_qkv_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_5_modules_attn_modules_proj_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_5_modules_norm2_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_5_modules_norm2_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_5_modules_mlp_modules_0_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_5_modules_mlp_modules_0_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_5_modules_mlp_modules_3_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_5_modules_mlp_modules_3_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_6_modules_norm1_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_6_modules_norm1_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_6_modules_attn_parameters_relative_position_bias_table_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_6_modules_attn_buffers_relative_position_index_: torch.Tensor,
+        L_self_modules_features_modules_5_modules_6_modules_attn_modules_qkv_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_6_modules_attn_modules_proj_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_6_modules_attn_modules_qkv_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_6_modules_attn_modules_proj_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_6_modules_norm2_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_6_modules_norm2_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_6_modules_mlp_modules_0_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_6_modules_mlp_modules_0_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_6_modules_mlp_modules_3_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_6_modules_mlp_modules_3_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_7_modules_norm1_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_7_modules_norm1_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_7_modules_attn_parameters_relative_position_bias_table_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_7_modules_attn_buffers_relative_position_index_: torch.Tensor,
+        L_self_modules_features_modules_5_modules_7_modules_attn_modules_qkv_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_7_modules_attn_modules_proj_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_7_modules_attn_modules_qkv_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_7_modules_attn_modules_proj_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_7_modules_norm2_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_7_modules_norm2_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_7_modules_mlp_modules_0_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_7_modules_mlp_modules_0_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_7_modules_mlp_modules_3_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_7_modules_mlp_modules_3_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_8_modules_norm1_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_8_modules_norm1_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_8_modules_attn_parameters_relative_position_bias_table_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_8_modules_attn_buffers_relative_position_index_: torch.Tensor,
+        L_self_modules_features_modules_5_modules_8_modules_attn_modules_qkv_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_8_modules_attn_modules_proj_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_8_modules_attn_modules_qkv_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_8_modules_attn_modules_proj_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_8_modules_norm2_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_8_modules_norm2_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_8_modules_mlp_modules_0_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_8_modules_mlp_modules_0_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_8_modules_mlp_modules_3_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_8_modules_mlp_modules_3_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_9_modules_norm1_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_9_modules_norm1_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_9_modules_attn_parameters_relative_position_bias_table_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_9_modules_attn_buffers_relative_position_index_: torch.Tensor,
+        L_self_modules_features_modules_5_modules_9_modules_attn_modules_qkv_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_9_modules_attn_modules_proj_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_9_modules_attn_modules_qkv_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_9_modules_attn_modules_proj_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_9_modules_norm2_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_9_modules_norm2_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_9_modules_mlp_modules_0_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_9_modules_mlp_modules_0_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_9_modules_mlp_modules_3_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_9_modules_mlp_modules_3_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_10_modules_norm1_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_10_modules_norm1_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_10_modules_attn_parameters_relative_position_bias_table_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_10_modules_attn_buffers_relative_position_index_: torch.Tensor,
+        L_self_modules_features_modules_5_modules_10_modules_attn_modules_qkv_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_10_modules_attn_modules_proj_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_10_modules_attn_modules_qkv_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_10_modules_attn_modules_proj_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_10_modules_norm2_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_10_modules_norm2_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_10_modules_mlp_modules_0_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_10_modules_mlp_modules_0_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_10_modules_mlp_modules_3_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_10_modules_mlp_modules_3_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_11_modules_norm1_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_11_modules_norm1_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_11_modules_attn_parameters_relative_position_bias_table_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_11_modules_attn_buffers_relative_position_index_: torch.Tensor,
+        L_self_modules_features_modules_5_modules_11_modules_attn_modules_qkv_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_11_modules_attn_modules_proj_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_11_modules_attn_modules_qkv_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_11_modules_attn_modules_proj_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_11_modules_norm2_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_11_modules_norm2_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_11_modules_mlp_modules_0_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_11_modules_mlp_modules_0_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_11_modules_mlp_modules_3_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_11_modules_mlp_modules_3_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_12_modules_norm1_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_12_modules_norm1_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_12_modules_attn_parameters_relative_position_bias_table_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_12_modules_attn_buffers_relative_position_index_: torch.Tensor,
+        L_self_modules_features_modules_5_modules_12_modules_attn_modules_qkv_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_12_modules_attn_modules_proj_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_12_modules_attn_modules_qkv_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_12_modules_attn_modules_proj_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_12_modules_norm2_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_12_modules_norm2_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_12_modules_mlp_modules_0_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_12_modules_mlp_modules_0_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_12_modules_mlp_modules_3_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_12_modules_mlp_modules_3_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_13_modules_norm1_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_13_modules_norm1_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_13_modules_attn_parameters_relative_position_bias_table_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_13_modules_attn_buffers_relative_position_index_: torch.Tensor,
+        L_self_modules_features_modules_5_modules_13_modules_attn_modules_qkv_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_13_modules_attn_modules_proj_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_13_modules_attn_modules_qkv_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_13_modules_attn_modules_proj_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_13_modules_norm2_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_13_modules_norm2_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_13_modules_mlp_modules_0_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_13_modules_mlp_modules_0_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_13_modules_mlp_modules_3_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_13_modules_mlp_modules_3_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_14_modules_norm1_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_14_modules_norm1_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_14_modules_attn_parameters_relative_position_bias_table_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_14_modules_attn_buffers_relative_position_index_: torch.Tensor,
+        L_self_modules_features_modules_5_modules_14_modules_attn_modules_qkv_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_14_modules_attn_modules_proj_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_14_modules_attn_modules_qkv_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_14_modules_attn_modules_proj_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_14_modules_norm2_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_14_modules_norm2_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_14_modules_mlp_modules_0_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_14_modules_mlp_modules_0_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_14_modules_mlp_modules_3_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_14_modules_mlp_modules_3_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_15_modules_norm1_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_15_modules_norm1_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_15_modules_attn_parameters_relative_position_bias_table_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_15_modules_attn_buffers_relative_position_index_: torch.Tensor,
+        L_self_modules_features_modules_5_modules_15_modules_attn_modules_qkv_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_15_modules_attn_modules_proj_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_15_modules_attn_modules_qkv_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_15_modules_attn_modules_proj_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_15_modules_norm2_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_15_modules_norm2_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_15_modules_mlp_modules_0_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_15_modules_mlp_modules_0_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_15_modules_mlp_modules_3_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_15_modules_mlp_modules_3_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_16_modules_norm1_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_16_modules_norm1_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_16_modules_attn_parameters_relative_position_bias_table_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_16_modules_attn_buffers_relative_position_index_: torch.Tensor,
+        L_self_modules_features_modules_5_modules_16_modules_attn_modules_qkv_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_16_modules_attn_modules_proj_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_16_modules_attn_modules_qkv_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_16_modules_attn_modules_proj_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_16_modules_norm2_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_16_modules_norm2_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_16_modules_mlp_modules_0_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_16_modules_mlp_modules_0_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_16_modules_mlp_modules_3_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_16_modules_mlp_modules_3_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_17_modules_norm1_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_17_modules_norm1_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_17_modules_attn_parameters_relative_position_bias_table_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_17_modules_attn_buffers_relative_position_index_: torch.Tensor,
+        L_self_modules_features_modules_5_modules_17_modules_attn_modules_qkv_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_17_modules_attn_modules_proj_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_17_modules_attn_modules_qkv_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_17_modules_attn_modules_proj_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_17_modules_norm2_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_17_modules_norm2_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_17_modules_mlp_modules_0_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_17_modules_mlp_modules_0_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_17_modules_mlp_modules_3_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_5_modules_17_modules_mlp_modules_3_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_6_modules_norm_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_6_modules_norm_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_6_modules_reduction_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_7_modules_0_modules_norm1_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_7_modules_0_modules_norm1_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_7_modules_0_modules_attn_parameters_relative_position_bias_table_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_7_modules_0_modules_attn_buffers_relative_position_index_: torch.Tensor,
+        L_self_modules_features_modules_7_modules_0_modules_attn_modules_qkv_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_7_modules_0_modules_attn_modules_proj_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_7_modules_0_modules_attn_modules_qkv_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_7_modules_0_modules_attn_modules_proj_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_7_modules_0_modules_norm2_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_7_modules_0_modules_norm2_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_7_modules_0_modules_mlp_modules_0_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_7_modules_0_modules_mlp_modules_0_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_7_modules_0_modules_mlp_modules_3_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_7_modules_0_modules_mlp_modules_3_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_7_modules_1_modules_norm1_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_7_modules_1_modules_norm1_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_7_modules_1_modules_attn_parameters_relative_position_bias_table_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_7_modules_1_modules_attn_buffers_relative_position_index_: torch.Tensor,
+        L_self_modules_features_modules_7_modules_1_modules_attn_modules_qkv_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_7_modules_1_modules_attn_modules_proj_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_7_modules_1_modules_attn_modules_qkv_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_7_modules_1_modules_attn_modules_proj_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_7_modules_1_modules_norm2_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_7_modules_1_modules_norm2_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_7_modules_1_modules_mlp_modules_0_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_7_modules_1_modules_mlp_modules_0_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_7_modules_1_modules_mlp_modules_3_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_features_modules_7_modules_1_modules_mlp_modules_3_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_norm_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_norm_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_head_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_head_parameters_bias_: torch.nn.parameter.Parameter,
+    ):
+        l_self_modules_features_modules_0_modules_0_parameters_weight_ = (
+            L_self_modules_features_modules_0_modules_0_parameters_weight_
+        )
+        l_self_modules_features_modules_0_modules_0_parameters_bias_ = (
+            L_self_modules_features_modules_0_modules_0_parameters_bias_
+        )
+        l_x_ = L_x_
+        l_self_modules_features_modules_0_modules_2_parameters_weight_ = (
+            L_self_modules_features_modules_0_modules_2_parameters_weight_
+        )
+        l_self_modules_features_modules_0_modules_2_parameters_bias_ = (
+            L_self_modules_features_modules_0_modules_2_parameters_bias_
+        )
+        l_self_modules_features_modules_1_modules_0_modules_norm1_parameters_weight_ = (
+            L_self_modules_features_modules_1_modules_0_modules_norm1_parameters_weight_
+        )
+        l_self_modules_features_modules_1_modules_0_modules_norm1_parameters_bias_ = (
+            L_self_modules_features_modules_1_modules_0_modules_norm1_parameters_bias_
+        )
+        l_self_modules_features_modules_1_modules_0_modules_attn_parameters_relative_position_bias_table_ = L_self_modules_features_modules_1_modules_0_modules_attn_parameters_relative_position_bias_table_
+        l_self_modules_features_modules_1_modules_0_modules_attn_buffers_relative_position_index_ = L_self_modules_features_modules_1_modules_0_modules_attn_buffers_relative_position_index_
+        l_self_modules_features_modules_1_modules_0_modules_attn_modules_qkv_parameters_weight_ = L_self_modules_features_modules_1_modules_0_modules_attn_modules_qkv_parameters_weight_
+        l_self_modules_features_modules_1_modules_0_modules_attn_modules_proj_parameters_weight_ = L_self_modules_features_modules_1_modules_0_modules_attn_modules_proj_parameters_weight_
+        l_self_modules_features_modules_1_modules_0_modules_attn_modules_qkv_parameters_bias_ = L_self_modules_features_modules_1_modules_0_modules_attn_modules_qkv_parameters_bias_
+        l_self_modules_features_modules_1_modules_0_modules_attn_modules_proj_parameters_bias_ = L_self_modules_features_modules_1_modules_0_modules_attn_modules_proj_parameters_bias_
+        l_self_modules_features_modules_1_modules_0_modules_norm2_parameters_weight_ = (
+            L_self_modules_features_modules_1_modules_0_modules_norm2_parameters_weight_
+        )
+        l_self_modules_features_modules_1_modules_0_modules_norm2_parameters_bias_ = (
+            L_self_modules_features_modules_1_modules_0_modules_norm2_parameters_bias_
+        )
+        l_self_modules_features_modules_1_modules_0_modules_mlp_modules_0_parameters_weight_ = L_self_modules_features_modules_1_modules_0_modules_mlp_modules_0_parameters_weight_
+        l_self_modules_features_modules_1_modules_0_modules_mlp_modules_0_parameters_bias_ = L_self_modules_features_modules_1_modules_0_modules_mlp_modules_0_parameters_bias_
+        l_self_modules_features_modules_1_modules_0_modules_mlp_modules_3_parameters_weight_ = L_self_modules_features_modules_1_modules_0_modules_mlp_modules_3_parameters_weight_
+        l_self_modules_features_modules_1_modules_0_modules_mlp_modules_3_parameters_bias_ = L_self_modules_features_modules_1_modules_0_modules_mlp_modules_3_parameters_bias_
+        l_self_modules_features_modules_1_modules_1_modules_norm1_parameters_weight_ = (
+            L_self_modules_features_modules_1_modules_1_modules_norm1_parameters_weight_
+        )
+        l_self_modules_features_modules_1_modules_1_modules_norm1_parameters_bias_ = (
+            L_self_modules_features_modules_1_modules_1_modules_norm1_parameters_bias_
+        )
+        l_self_modules_features_modules_1_modules_1_modules_attn_parameters_relative_position_bias_table_ = L_self_modules_features_modules_1_modules_1_modules_attn_parameters_relative_position_bias_table_
+        l_self_modules_features_modules_1_modules_1_modules_attn_buffers_relative_position_index_ = L_self_modules_features_modules_1_modules_1_modules_attn_buffers_relative_position_index_
+        l_self_modules_features_modules_1_modules_1_modules_attn_modules_qkv_parameters_weight_ = L_self_modules_features_modules_1_modules_1_modules_attn_modules_qkv_parameters_weight_
+        l_self_modules_features_modules_1_modules_1_modules_attn_modules_proj_parameters_weight_ = L_self_modules_features_modules_1_modules_1_modules_attn_modules_proj_parameters_weight_
+        l_self_modules_features_modules_1_modules_1_modules_attn_modules_qkv_parameters_bias_ = L_self_modules_features_modules_1_modules_1_modules_attn_modules_qkv_parameters_bias_
+        l_self_modules_features_modules_1_modules_1_modules_attn_modules_proj_parameters_bias_ = L_self_modules_features_modules_1_modules_1_modules_attn_modules_proj_parameters_bias_
+        l_self_modules_features_modules_1_modules_1_modules_norm2_parameters_weight_ = (
+            L_self_modules_features_modules_1_modules_1_modules_norm2_parameters_weight_
+        )
+        l_self_modules_features_modules_1_modules_1_modules_norm2_parameters_bias_ = (
+            L_self_modules_features_modules_1_modules_1_modules_norm2_parameters_bias_
+        )
+        l_self_modules_features_modules_1_modules_1_modules_mlp_modules_0_parameters_weight_ = L_self_modules_features_modules_1_modules_1_modules_mlp_modules_0_parameters_weight_
+        l_self_modules_features_modules_1_modules_1_modules_mlp_modules_0_parameters_bias_ = L_self_modules_features_modules_1_modules_1_modules_mlp_modules_0_parameters_bias_
+        l_self_modules_features_modules_1_modules_1_modules_mlp_modules_3_parameters_weight_ = L_self_modules_features_modules_1_modules_1_modules_mlp_modules_3_parameters_weight_
+        l_self_modules_features_modules_1_modules_1_modules_mlp_modules_3_parameters_bias_ = L_self_modules_features_modules_1_modules_1_modules_mlp_modules_3_parameters_bias_
+        l_self_modules_features_modules_2_modules_norm_parameters_weight_ = (
+            L_self_modules_features_modules_2_modules_norm_parameters_weight_
+        )
+        l_self_modules_features_modules_2_modules_norm_parameters_bias_ = (
+            L_self_modules_features_modules_2_modules_norm_parameters_bias_
+        )
+        l_self_modules_features_modules_2_modules_reduction_parameters_weight_ = (
+            L_self_modules_features_modules_2_modules_reduction_parameters_weight_
+        )
+        l_self_modules_features_modules_3_modules_0_modules_norm1_parameters_weight_ = (
+            L_self_modules_features_modules_3_modules_0_modules_norm1_parameters_weight_
+        )
+        l_self_modules_features_modules_3_modules_0_modules_norm1_parameters_bias_ = (
+            L_self_modules_features_modules_3_modules_0_modules_norm1_parameters_bias_
+        )
+        l_self_modules_features_modules_3_modules_0_modules_attn_parameters_relative_position_bias_table_ = L_self_modules_features_modules_3_modules_0_modules_attn_parameters_relative_position_bias_table_
+        l_self_modules_features_modules_3_modules_0_modules_attn_buffers_relative_position_index_ = L_self_modules_features_modules_3_modules_0_modules_attn_buffers_relative_position_index_
+        l_self_modules_features_modules_3_modules_0_modules_attn_modules_qkv_parameters_weight_ = L_self_modules_features_modules_3_modules_0_modules_attn_modules_qkv_parameters_weight_
+        l_self_modules_features_modules_3_modules_0_modules_attn_modules_proj_parameters_weight_ = L_self_modules_features_modules_3_modules_0_modules_attn_modules_proj_parameters_weight_
+        l_self_modules_features_modules_3_modules_0_modules_attn_modules_qkv_parameters_bias_ = L_self_modules_features_modules_3_modules_0_modules_attn_modules_qkv_parameters_bias_
+        l_self_modules_features_modules_3_modules_0_modules_attn_modules_proj_parameters_bias_ = L_self_modules_features_modules_3_modules_0_modules_attn_modules_proj_parameters_bias_
+        l_self_modules_features_modules_3_modules_0_modules_norm2_parameters_weight_ = (
+            L_self_modules_features_modules_3_modules_0_modules_norm2_parameters_weight_
+        )
+        l_self_modules_features_modules_3_modules_0_modules_norm2_parameters_bias_ = (
+            L_self_modules_features_modules_3_modules_0_modules_norm2_parameters_bias_
+        )
+        l_self_modules_features_modules_3_modules_0_modules_mlp_modules_0_parameters_weight_ = L_self_modules_features_modules_3_modules_0_modules_mlp_modules_0_parameters_weight_
+        l_self_modules_features_modules_3_modules_0_modules_mlp_modules_0_parameters_bias_ = L_self_modules_features_modules_3_modules_0_modules_mlp_modules_0_parameters_bias_
+        l_self_modules_features_modules_3_modules_0_modules_mlp_modules_3_parameters_weight_ = L_self_modules_features_modules_3_modules_0_modules_mlp_modules_3_parameters_weight_
+        l_self_modules_features_modules_3_modules_0_modules_mlp_modules_3_parameters_bias_ = L_self_modules_features_modules_3_modules_0_modules_mlp_modules_3_parameters_bias_
+        l_self_modules_features_modules_3_modules_1_modules_norm1_parameters_weight_ = (
+            L_self_modules_features_modules_3_modules_1_modules_norm1_parameters_weight_
+        )
+        l_self_modules_features_modules_3_modules_1_modules_norm1_parameters_bias_ = (
+            L_self_modules_features_modules_3_modules_1_modules_norm1_parameters_bias_
+        )
+        l_self_modules_features_modules_3_modules_1_modules_attn_parameters_relative_position_bias_table_ = L_self_modules_features_modules_3_modules_1_modules_attn_parameters_relative_position_bias_table_
+        l_self_modules_features_modules_3_modules_1_modules_attn_buffers_relative_position_index_ = L_self_modules_features_modules_3_modules_1_modules_attn_buffers_relative_position_index_
+        l_self_modules_features_modules_3_modules_1_modules_attn_modules_qkv_parameters_weight_ = L_self_modules_features_modules_3_modules_1_modules_attn_modules_qkv_parameters_weight_
+        l_self_modules_features_modules_3_modules_1_modules_attn_modules_proj_parameters_weight_ = L_self_modules_features_modules_3_modules_1_modules_attn_modules_proj_parameters_weight_
+        l_self_modules_features_modules_3_modules_1_modules_attn_modules_qkv_parameters_bias_ = L_self_modules_features_modules_3_modules_1_modules_attn_modules_qkv_parameters_bias_
+        l_self_modules_features_modules_3_modules_1_modules_attn_modules_proj_parameters_bias_ = L_self_modules_features_modules_3_modules_1_modules_attn_modules_proj_parameters_bias_
+        l_self_modules_features_modules_3_modules_1_modules_norm2_parameters_weight_ = (
+            L_self_modules_features_modules_3_modules_1_modules_norm2_parameters_weight_
+        )
+        l_self_modules_features_modules_3_modules_1_modules_norm2_parameters_bias_ = (
+            L_self_modules_features_modules_3_modules_1_modules_norm2_parameters_bias_
+        )
+        l_self_modules_features_modules_3_modules_1_modules_mlp_modules_0_parameters_weight_ = L_self_modules_features_modules_3_modules_1_modules_mlp_modules_0_parameters_weight_
+        l_self_modules_features_modules_3_modules_1_modules_mlp_modules_0_parameters_bias_ = L_self_modules_features_modules_3_modules_1_modules_mlp_modules_0_parameters_bias_
+        l_self_modules_features_modules_3_modules_1_modules_mlp_modules_3_parameters_weight_ = L_self_modules_features_modules_3_modules_1_modules_mlp_modules_3_parameters_weight_
+        l_self_modules_features_modules_3_modules_1_modules_mlp_modules_3_parameters_bias_ = L_self_modules_features_modules_3_modules_1_modules_mlp_modules_3_parameters_bias_
+        l_self_modules_features_modules_4_modules_norm_parameters_weight_ = (
+            L_self_modules_features_modules_4_modules_norm_parameters_weight_
+        )
+        l_self_modules_features_modules_4_modules_norm_parameters_bias_ = (
+            L_self_modules_features_modules_4_modules_norm_parameters_bias_
+        )
+        l_self_modules_features_modules_4_modules_reduction_parameters_weight_ = (
+            L_self_modules_features_modules_4_modules_reduction_parameters_weight_
+        )
+        l_self_modules_features_modules_5_modules_0_modules_norm1_parameters_weight_ = (
+            L_self_modules_features_modules_5_modules_0_modules_norm1_parameters_weight_
+        )
+        l_self_modules_features_modules_5_modules_0_modules_norm1_parameters_bias_ = (
+            L_self_modules_features_modules_5_modules_0_modules_norm1_parameters_bias_
+        )
+        l_self_modules_features_modules_5_modules_0_modules_attn_parameters_relative_position_bias_table_ = L_self_modules_features_modules_5_modules_0_modules_attn_parameters_relative_position_bias_table_
+        l_self_modules_features_modules_5_modules_0_modules_attn_buffers_relative_position_index_ = L_self_modules_features_modules_5_modules_0_modules_attn_buffers_relative_position_index_
+        l_self_modules_features_modules_5_modules_0_modules_attn_modules_qkv_parameters_weight_ = L_self_modules_features_modules_5_modules_0_modules_attn_modules_qkv_parameters_weight_
+        l_self_modules_features_modules_5_modules_0_modules_attn_modules_proj_parameters_weight_ = L_self_modules_features_modules_5_modules_0_modules_attn_modules_proj_parameters_weight_
+        l_self_modules_features_modules_5_modules_0_modules_attn_modules_qkv_parameters_bias_ = L_self_modules_features_modules_5_modules_0_modules_attn_modules_qkv_parameters_bias_
+        l_self_modules_features_modules_5_modules_0_modules_attn_modules_proj_parameters_bias_ = L_self_modules_features_modules_5_modules_0_modules_attn_modules_proj_parameters_bias_
+        l_self_modules_features_modules_5_modules_0_modules_norm2_parameters_weight_ = (
+            L_self_modules_features_modules_5_modules_0_modules_norm2_parameters_weight_
+        )
+        l_self_modules_features_modules_5_modules_0_modules_norm2_parameters_bias_ = (
+            L_self_modules_features_modules_5_modules_0_modules_norm2_parameters_bias_
+        )
+        l_self_modules_features_modules_5_modules_0_modules_mlp_modules_0_parameters_weight_ = L_self_modules_features_modules_5_modules_0_modules_mlp_modules_0_parameters_weight_
+        l_self_modules_features_modules_5_modules_0_modules_mlp_modules_0_parameters_bias_ = L_self_modules_features_modules_5_modules_0_modules_mlp_modules_0_parameters_bias_
+        l_self_modules_features_modules_5_modules_0_modules_mlp_modules_3_parameters_weight_ = L_self_modules_features_modules_5_modules_0_modules_mlp_modules_3_parameters_weight_
+        l_self_modules_features_modules_5_modules_0_modules_mlp_modules_3_parameters_bias_ = L_self_modules_features_modules_5_modules_0_modules_mlp_modules_3_parameters_bias_
+        l_self_modules_features_modules_5_modules_1_modules_norm1_parameters_weight_ = (
+            L_self_modules_features_modules_5_modules_1_modules_norm1_parameters_weight_
+        )
+        l_self_modules_features_modules_5_modules_1_modules_norm1_parameters_bias_ = (
+            L_self_modules_features_modules_5_modules_1_modules_norm1_parameters_bias_
+        )
+        l_self_modules_features_modules_5_modules_1_modules_attn_parameters_relative_position_bias_table_ = L_self_modules_features_modules_5_modules_1_modules_attn_parameters_relative_position_bias_table_
+        l_self_modules_features_modules_5_modules_1_modules_attn_buffers_relative_position_index_ = L_self_modules_features_modules_5_modules_1_modules_attn_buffers_relative_position_index_
+        l_self_modules_features_modules_5_modules_1_modules_attn_modules_qkv_parameters_weight_ = L_self_modules_features_modules_5_modules_1_modules_attn_modules_qkv_parameters_weight_
+        l_self_modules_features_modules_5_modules_1_modules_attn_modules_proj_parameters_weight_ = L_self_modules_features_modules_5_modules_1_modules_attn_modules_proj_parameters_weight_
+        l_self_modules_features_modules_5_modules_1_modules_attn_modules_qkv_parameters_bias_ = L_self_modules_features_modules_5_modules_1_modules_attn_modules_qkv_parameters_bias_
+        l_self_modules_features_modules_5_modules_1_modules_attn_modules_proj_parameters_bias_ = L_self_modules_features_modules_5_modules_1_modules_attn_modules_proj_parameters_bias_
+        l_self_modules_features_modules_5_modules_1_modules_norm2_parameters_weight_ = (
+            L_self_modules_features_modules_5_modules_1_modules_norm2_parameters_weight_
+        )
+        l_self_modules_features_modules_5_modules_1_modules_norm2_parameters_bias_ = (
+            L_self_modules_features_modules_5_modules_1_modules_norm2_parameters_bias_
+        )
+        l_self_modules_features_modules_5_modules_1_modules_mlp_modules_0_parameters_weight_ = L_self_modules_features_modules_5_modules_1_modules_mlp_modules_0_parameters_weight_
+        l_self_modules_features_modules_5_modules_1_modules_mlp_modules_0_parameters_bias_ = L_self_modules_features_modules_5_modules_1_modules_mlp_modules_0_parameters_bias_
+        l_self_modules_features_modules_5_modules_1_modules_mlp_modules_3_parameters_weight_ = L_self_modules_features_modules_5_modules_1_modules_mlp_modules_3_parameters_weight_
+        l_self_modules_features_modules_5_modules_1_modules_mlp_modules_3_parameters_bias_ = L_self_modules_features_modules_5_modules_1_modules_mlp_modules_3_parameters_bias_
+        l_self_modules_features_modules_5_modules_2_modules_norm1_parameters_weight_ = (
+            L_self_modules_features_modules_5_modules_2_modules_norm1_parameters_weight_
+        )
+        l_self_modules_features_modules_5_modules_2_modules_norm1_parameters_bias_ = (
+            L_self_modules_features_modules_5_modules_2_modules_norm1_parameters_bias_
+        )
+        l_self_modules_features_modules_5_modules_2_modules_attn_parameters_relative_position_bias_table_ = L_self_modules_features_modules_5_modules_2_modules_attn_parameters_relative_position_bias_table_
+        l_self_modules_features_modules_5_modules_2_modules_attn_buffers_relative_position_index_ = L_self_modules_features_modules_5_modules_2_modules_attn_buffers_relative_position_index_
+        l_self_modules_features_modules_5_modules_2_modules_attn_modules_qkv_parameters_weight_ = L_self_modules_features_modules_5_modules_2_modules_attn_modules_qkv_parameters_weight_
+        l_self_modules_features_modules_5_modules_2_modules_attn_modules_proj_parameters_weight_ = L_self_modules_features_modules_5_modules_2_modules_attn_modules_proj_parameters_weight_
+        l_self_modules_features_modules_5_modules_2_modules_attn_modules_qkv_parameters_bias_ = L_self_modules_features_modules_5_modules_2_modules_attn_modules_qkv_parameters_bias_
+        l_self_modules_features_modules_5_modules_2_modules_attn_modules_proj_parameters_bias_ = L_self_modules_features_modules_5_modules_2_modules_attn_modules_proj_parameters_bias_
+        l_self_modules_features_modules_5_modules_2_modules_norm2_parameters_weight_ = (
+            L_self_modules_features_modules_5_modules_2_modules_norm2_parameters_weight_
+        )
+        l_self_modules_features_modules_5_modules_2_modules_norm2_parameters_bias_ = (
+            L_self_modules_features_modules_5_modules_2_modules_norm2_parameters_bias_
+        )
+        l_self_modules_features_modules_5_modules_2_modules_mlp_modules_0_parameters_weight_ = L_self_modules_features_modules_5_modules_2_modules_mlp_modules_0_parameters_weight_
+        l_self_modules_features_modules_5_modules_2_modules_mlp_modules_0_parameters_bias_ = L_self_modules_features_modules_5_modules_2_modules_mlp_modules_0_parameters_bias_
+        l_self_modules_features_modules_5_modules_2_modules_mlp_modules_3_parameters_weight_ = L_self_modules_features_modules_5_modules_2_modules_mlp_modules_3_parameters_weight_
+        l_self_modules_features_modules_5_modules_2_modules_mlp_modules_3_parameters_bias_ = L_self_modules_features_modules_5_modules_2_modules_mlp_modules_3_parameters_bias_
+        l_self_modules_features_modules_5_modules_3_modules_norm1_parameters_weight_ = (
+            L_self_modules_features_modules_5_modules_3_modules_norm1_parameters_weight_
+        )
+        l_self_modules_features_modules_5_modules_3_modules_norm1_parameters_bias_ = (
+            L_self_modules_features_modules_5_modules_3_modules_norm1_parameters_bias_
+        )
+        l_self_modules_features_modules_5_modules_3_modules_attn_parameters_relative_position_bias_table_ = L_self_modules_features_modules_5_modules_3_modules_attn_parameters_relative_position_bias_table_
+        l_self_modules_features_modules_5_modules_3_modules_attn_buffers_relative_position_index_ = L_self_modules_features_modules_5_modules_3_modules_attn_buffers_relative_position_index_
+        l_self_modules_features_modules_5_modules_3_modules_attn_modules_qkv_parameters_weight_ = L_self_modules_features_modules_5_modules_3_modules_attn_modules_qkv_parameters_weight_
+        l_self_modules_features_modules_5_modules_3_modules_attn_modules_proj_parameters_weight_ = L_self_modules_features_modules_5_modules_3_modules_attn_modules_proj_parameters_weight_
+        l_self_modules_features_modules_5_modules_3_modules_attn_modules_qkv_parameters_bias_ = L_self_modules_features_modules_5_modules_3_modules_attn_modules_qkv_parameters_bias_
+        l_self_modules_features_modules_5_modules_3_modules_attn_modules_proj_parameters_bias_ = L_self_modules_features_modules_5_modules_3_modules_attn_modules_proj_parameters_bias_
+        l_self_modules_features_modules_5_modules_3_modules_norm2_parameters_weight_ = (
+            L_self_modules_features_modules_5_modules_3_modules_norm2_parameters_weight_
+        )
+        l_self_modules_features_modules_5_modules_3_modules_norm2_parameters_bias_ = (
+            L_self_modules_features_modules_5_modules_3_modules_norm2_parameters_bias_
+        )
+        l_self_modules_features_modules_5_modules_3_modules_mlp_modules_0_parameters_weight_ = L_self_modules_features_modules_5_modules_3_modules_mlp_modules_0_parameters_weight_
+        l_self_modules_features_modules_5_modules_3_modules_mlp_modules_0_parameters_bias_ = L_self_modules_features_modules_5_modules_3_modules_mlp_modules_0_parameters_bias_
+        l_self_modules_features_modules_5_modules_3_modules_mlp_modules_3_parameters_weight_ = L_self_modules_features_modules_5_modules_3_modules_mlp_modules_3_parameters_weight_
+        l_self_modules_features_modules_5_modules_3_modules_mlp_modules_3_parameters_bias_ = L_self_modules_features_modules_5_modules_3_modules_mlp_modules_3_parameters_bias_
+        l_self_modules_features_modules_5_modules_4_modules_norm1_parameters_weight_ = (
+            L_self_modules_features_modules_5_modules_4_modules_norm1_parameters_weight_
+        )
+        l_self_modules_features_modules_5_modules_4_modules_norm1_parameters_bias_ = (
+            L_self_modules_features_modules_5_modules_4_modules_norm1_parameters_bias_
+        )
+        l_self_modules_features_modules_5_modules_4_modules_attn_parameters_relative_position_bias_table_ = L_self_modules_features_modules_5_modules_4_modules_attn_parameters_relative_position_bias_table_
+        l_self_modules_features_modules_5_modules_4_modules_attn_buffers_relative_position_index_ = L_self_modules_features_modules_5_modules_4_modules_attn_buffers_relative_position_index_
+        l_self_modules_features_modules_5_modules_4_modules_attn_modules_qkv_parameters_weight_ = L_self_modules_features_modules_5_modules_4_modules_attn_modules_qkv_parameters_weight_
+        l_self_modules_features_modules_5_modules_4_modules_attn_modules_proj_parameters_weight_ = L_self_modules_features_modules_5_modules_4_modules_attn_modules_proj_parameters_weight_
+        l_self_modules_features_modules_5_modules_4_modules_attn_modules_qkv_parameters_bias_ = L_self_modules_features_modules_5_modules_4_modules_attn_modules_qkv_parameters_bias_
+        l_self_modules_features_modules_5_modules_4_modules_attn_modules_proj_parameters_bias_ = L_self_modules_features_modules_5_modules_4_modules_attn_modules_proj_parameters_bias_
+        l_self_modules_features_modules_5_modules_4_modules_norm2_parameters_weight_ = (
+            L_self_modules_features_modules_5_modules_4_modules_norm2_parameters_weight_
+        )
+        l_self_modules_features_modules_5_modules_4_modules_norm2_parameters_bias_ = (
+            L_self_modules_features_modules_5_modules_4_modules_norm2_parameters_bias_
+        )
+        l_self_modules_features_modules_5_modules_4_modules_mlp_modules_0_parameters_weight_ = L_self_modules_features_modules_5_modules_4_modules_mlp_modules_0_parameters_weight_
+        l_self_modules_features_modules_5_modules_4_modules_mlp_modules_0_parameters_bias_ = L_self_modules_features_modules_5_modules_4_modules_mlp_modules_0_parameters_bias_
+        l_self_modules_features_modules_5_modules_4_modules_mlp_modules_3_parameters_weight_ = L_self_modules_features_modules_5_modules_4_modules_mlp_modules_3_parameters_weight_
+        l_self_modules_features_modules_5_modules_4_modules_mlp_modules_3_parameters_bias_ = L_self_modules_features_modules_5_modules_4_modules_mlp_modules_3_parameters_bias_
+        l_self_modules_features_modules_5_modules_5_modules_norm1_parameters_weight_ = (
+            L_self_modules_features_modules_5_modules_5_modules_norm1_parameters_weight_
+        )
+        l_self_modules_features_modules_5_modules_5_modules_norm1_parameters_bias_ = (
+            L_self_modules_features_modules_5_modules_5_modules_norm1_parameters_bias_
+        )
+        l_self_modules_features_modules_5_modules_5_modules_attn_parameters_relative_position_bias_table_ = L_self_modules_features_modules_5_modules_5_modules_attn_parameters_relative_position_bias_table_
+        l_self_modules_features_modules_5_modules_5_modules_attn_buffers_relative_position_index_ = L_self_modules_features_modules_5_modules_5_modules_attn_buffers_relative_position_index_
+        l_self_modules_features_modules_5_modules_5_modules_attn_modules_qkv_parameters_weight_ = L_self_modules_features_modules_5_modules_5_modules_attn_modules_qkv_parameters_weight_
+        l_self_modules_features_modules_5_modules_5_modules_attn_modules_proj_parameters_weight_ = L_self_modules_features_modules_5_modules_5_modules_attn_modules_proj_parameters_weight_
+        l_self_modules_features_modules_5_modules_5_modules_attn_modules_qkv_parameters_bias_ = L_self_modules_features_modules_5_modules_5_modules_attn_modules_qkv_parameters_bias_
+        l_self_modules_features_modules_5_modules_5_modules_attn_modules_proj_parameters_bias_ = L_self_modules_features_modules_5_modules_5_modules_attn_modules_proj_parameters_bias_
+        l_self_modules_features_modules_5_modules_5_modules_norm2_parameters_weight_ = (
+            L_self_modules_features_modules_5_modules_5_modules_norm2_parameters_weight_
+        )
+        l_self_modules_features_modules_5_modules_5_modules_norm2_parameters_bias_ = (
+            L_self_modules_features_modules_5_modules_5_modules_norm2_parameters_bias_
+        )
+        l_self_modules_features_modules_5_modules_5_modules_mlp_modules_0_parameters_weight_ = L_self_modules_features_modules_5_modules_5_modules_mlp_modules_0_parameters_weight_
+        l_self_modules_features_modules_5_modules_5_modules_mlp_modules_0_parameters_bias_ = L_self_modules_features_modules_5_modules_5_modules_mlp_modules_0_parameters_bias_
+        l_self_modules_features_modules_5_modules_5_modules_mlp_modules_3_parameters_weight_ = L_self_modules_features_modules_5_modules_5_modules_mlp_modules_3_parameters_weight_
+        l_self_modules_features_modules_5_modules_5_modules_mlp_modules_3_parameters_bias_ = L_self_modules_features_modules_5_modules_5_modules_mlp_modules_3_parameters_bias_
+        l_self_modules_features_modules_5_modules_6_modules_norm1_parameters_weight_ = (
+            L_self_modules_features_modules_5_modules_6_modules_norm1_parameters_weight_
+        )
+        l_self_modules_features_modules_5_modules_6_modules_norm1_parameters_bias_ = (
+            L_self_modules_features_modules_5_modules_6_modules_norm1_parameters_bias_
+        )
+        l_self_modules_features_modules_5_modules_6_modules_attn_parameters_relative_position_bias_table_ = L_self_modules_features_modules_5_modules_6_modules_attn_parameters_relative_position_bias_table_
+        l_self_modules_features_modules_5_modules_6_modules_attn_buffers_relative_position_index_ = L_self_modules_features_modules_5_modules_6_modules_attn_buffers_relative_position_index_
+        l_self_modules_features_modules_5_modules_6_modules_attn_modules_qkv_parameters_weight_ = L_self_modules_features_modules_5_modules_6_modules_attn_modules_qkv_parameters_weight_
+        l_self_modules_features_modules_5_modules_6_modules_attn_modules_proj_parameters_weight_ = L_self_modules_features_modules_5_modules_6_modules_attn_modules_proj_parameters_weight_
+        l_self_modules_features_modules_5_modules_6_modules_attn_modules_qkv_parameters_bias_ = L_self_modules_features_modules_5_modules_6_modules_attn_modules_qkv_parameters_bias_
+        l_self_modules_features_modules_5_modules_6_modules_attn_modules_proj_parameters_bias_ = L_self_modules_features_modules_5_modules_6_modules_attn_modules_proj_parameters_bias_
+        l_self_modules_features_modules_5_modules_6_modules_norm2_parameters_weight_ = (
+            L_self_modules_features_modules_5_modules_6_modules_norm2_parameters_weight_
+        )
+        l_self_modules_features_modules_5_modules_6_modules_norm2_parameters_bias_ = (
+            L_self_modules_features_modules_5_modules_6_modules_norm2_parameters_bias_
+        )
+        l_self_modules_features_modules_5_modules_6_modules_mlp_modules_0_parameters_weight_ = L_self_modules_features_modules_5_modules_6_modules_mlp_modules_0_parameters_weight_
+        l_self_modules_features_modules_5_modules_6_modules_mlp_modules_0_parameters_bias_ = L_self_modules_features_modules_5_modules_6_modules_mlp_modules_0_parameters_bias_
+        l_self_modules_features_modules_5_modules_6_modules_mlp_modules_3_parameters_weight_ = L_self_modules_features_modules_5_modules_6_modules_mlp_modules_3_parameters_weight_
+        l_self_modules_features_modules_5_modules_6_modules_mlp_modules_3_parameters_bias_ = L_self_modules_features_modules_5_modules_6_modules_mlp_modules_3_parameters_bias_
+        l_self_modules_features_modules_5_modules_7_modules_norm1_parameters_weight_ = (
+            L_self_modules_features_modules_5_modules_7_modules_norm1_parameters_weight_
+        )
+        l_self_modules_features_modules_5_modules_7_modules_norm1_parameters_bias_ = (
+            L_self_modules_features_modules_5_modules_7_modules_norm1_parameters_bias_
+        )
+        l_self_modules_features_modules_5_modules_7_modules_attn_parameters_relative_position_bias_table_ = L_self_modules_features_modules_5_modules_7_modules_attn_parameters_relative_position_bias_table_
+        l_self_modules_features_modules_5_modules_7_modules_attn_buffers_relative_position_index_ = L_self_modules_features_modules_5_modules_7_modules_attn_buffers_relative_position_index_
+        l_self_modules_features_modules_5_modules_7_modules_attn_modules_qkv_parameters_weight_ = L_self_modules_features_modules_5_modules_7_modules_attn_modules_qkv_parameters_weight_
+        l_self_modules_features_modules_5_modules_7_modules_attn_modules_proj_parameters_weight_ = L_self_modules_features_modules_5_modules_7_modules_attn_modules_proj_parameters_weight_
+        l_self_modules_features_modules_5_modules_7_modules_attn_modules_qkv_parameters_bias_ = L_self_modules_features_modules_5_modules_7_modules_attn_modules_qkv_parameters_bias_
+        l_self_modules_features_modules_5_modules_7_modules_attn_modules_proj_parameters_bias_ = L_self_modules_features_modules_5_modules_7_modules_attn_modules_proj_parameters_bias_
+        l_self_modules_features_modules_5_modules_7_modules_norm2_parameters_weight_ = (
+            L_self_modules_features_modules_5_modules_7_modules_norm2_parameters_weight_
+        )
+        l_self_modules_features_modules_5_modules_7_modules_norm2_parameters_bias_ = (
+            L_self_modules_features_modules_5_modules_7_modules_norm2_parameters_bias_
+        )
+        l_self_modules_features_modules_5_modules_7_modules_mlp_modules_0_parameters_weight_ = L_self_modules_features_modules_5_modules_7_modules_mlp_modules_0_parameters_weight_
+        l_self_modules_features_modules_5_modules_7_modules_mlp_modules_0_parameters_bias_ = L_self_modules_features_modules_5_modules_7_modules_mlp_modules_0_parameters_bias_
+        l_self_modules_features_modules_5_modules_7_modules_mlp_modules_3_parameters_weight_ = L_self_modules_features_modules_5_modules_7_modules_mlp_modules_3_parameters_weight_
+        l_self_modules_features_modules_5_modules_7_modules_mlp_modules_3_parameters_bias_ = L_self_modules_features_modules_5_modules_7_modules_mlp_modules_3_parameters_bias_
+        l_self_modules_features_modules_5_modules_8_modules_norm1_parameters_weight_ = (
+            L_self_modules_features_modules_5_modules_8_modules_norm1_parameters_weight_
+        )
+        l_self_modules_features_modules_5_modules_8_modules_norm1_parameters_bias_ = (
+            L_self_modules_features_modules_5_modules_8_modules_norm1_parameters_bias_
+        )
+        l_self_modules_features_modules_5_modules_8_modules_attn_parameters_relative_position_bias_table_ = L_self_modules_features_modules_5_modules_8_modules_attn_parameters_relative_position_bias_table_
+        l_self_modules_features_modules_5_modules_8_modules_attn_buffers_relative_position_index_ = L_self_modules_features_modules_5_modules_8_modules_attn_buffers_relative_position_index_
+        l_self_modules_features_modules_5_modules_8_modules_attn_modules_qkv_parameters_weight_ = L_self_modules_features_modules_5_modules_8_modules_attn_modules_qkv_parameters_weight_
+        l_self_modules_features_modules_5_modules_8_modules_attn_modules_proj_parameters_weight_ = L_self_modules_features_modules_5_modules_8_modules_attn_modules_proj_parameters_weight_
+        l_self_modules_features_modules_5_modules_8_modules_attn_modules_qkv_parameters_bias_ = L_self_modules_features_modules_5_modules_8_modules_attn_modules_qkv_parameters_bias_
+        l_self_modules_features_modules_5_modules_8_modules_attn_modules_proj_parameters_bias_ = L_self_modules_features_modules_5_modules_8_modules_attn_modules_proj_parameters_bias_
+        l_self_modules_features_modules_5_modules_8_modules_norm2_parameters_weight_ = (
+            L_self_modules_features_modules_5_modules_8_modules_norm2_parameters_weight_
+        )
+        l_self_modules_features_modules_5_modules_8_modules_norm2_parameters_bias_ = (
+            L_self_modules_features_modules_5_modules_8_modules_norm2_parameters_bias_
+        )
+        l_self_modules_features_modules_5_modules_8_modules_mlp_modules_0_parameters_weight_ = L_self_modules_features_modules_5_modules_8_modules_mlp_modules_0_parameters_weight_
+        l_self_modules_features_modules_5_modules_8_modules_mlp_modules_0_parameters_bias_ = L_self_modules_features_modules_5_modules_8_modules_mlp_modules_0_parameters_bias_
+        l_self_modules_features_modules_5_modules_8_modules_mlp_modules_3_parameters_weight_ = L_self_modules_features_modules_5_modules_8_modules_mlp_modules_3_parameters_weight_
+        l_self_modules_features_modules_5_modules_8_modules_mlp_modules_3_parameters_bias_ = L_self_modules_features_modules_5_modules_8_modules_mlp_modules_3_parameters_bias_
+        l_self_modules_features_modules_5_modules_9_modules_norm1_parameters_weight_ = (
+            L_self_modules_features_modules_5_modules_9_modules_norm1_parameters_weight_
+        )
+        l_self_modules_features_modules_5_modules_9_modules_norm1_parameters_bias_ = (
+            L_self_modules_features_modules_5_modules_9_modules_norm1_parameters_bias_
+        )
+        l_self_modules_features_modules_5_modules_9_modules_attn_parameters_relative_position_bias_table_ = L_self_modules_features_modules_5_modules_9_modules_attn_parameters_relative_position_bias_table_
+        l_self_modules_features_modules_5_modules_9_modules_attn_buffers_relative_position_index_ = L_self_modules_features_modules_5_modules_9_modules_attn_buffers_relative_position_index_
+        l_self_modules_features_modules_5_modules_9_modules_attn_modules_qkv_parameters_weight_ = L_self_modules_features_modules_5_modules_9_modules_attn_modules_qkv_parameters_weight_
+        l_self_modules_features_modules_5_modules_9_modules_attn_modules_proj_parameters_weight_ = L_self_modules_features_modules_5_modules_9_modules_attn_modules_proj_parameters_weight_
+        l_self_modules_features_modules_5_modules_9_modules_attn_modules_qkv_parameters_bias_ = L_self_modules_features_modules_5_modules_9_modules_attn_modules_qkv_parameters_bias_
+        l_self_modules_features_modules_5_modules_9_modules_attn_modules_proj_parameters_bias_ = L_self_modules_features_modules_5_modules_9_modules_attn_modules_proj_parameters_bias_
+        l_self_modules_features_modules_5_modules_9_modules_norm2_parameters_weight_ = (
+            L_self_modules_features_modules_5_modules_9_modules_norm2_parameters_weight_
+        )
+        l_self_modules_features_modules_5_modules_9_modules_norm2_parameters_bias_ = (
+            L_self_modules_features_modules_5_modules_9_modules_norm2_parameters_bias_
+        )
+        l_self_modules_features_modules_5_modules_9_modules_mlp_modules_0_parameters_weight_ = L_self_modules_features_modules_5_modules_9_modules_mlp_modules_0_parameters_weight_
+        l_self_modules_features_modules_5_modules_9_modules_mlp_modules_0_parameters_bias_ = L_self_modules_features_modules_5_modules_9_modules_mlp_modules_0_parameters_bias_
+        l_self_modules_features_modules_5_modules_9_modules_mlp_modules_3_parameters_weight_ = L_self_modules_features_modules_5_modules_9_modules_mlp_modules_3_parameters_weight_
+        l_self_modules_features_modules_5_modules_9_modules_mlp_modules_3_parameters_bias_ = L_self_modules_features_modules_5_modules_9_modules_mlp_modules_3_parameters_bias_
+        l_self_modules_features_modules_5_modules_10_modules_norm1_parameters_weight_ = L_self_modules_features_modules_5_modules_10_modules_norm1_parameters_weight_
+        l_self_modules_features_modules_5_modules_10_modules_norm1_parameters_bias_ = (
+            L_self_modules_features_modules_5_modules_10_modules_norm1_parameters_bias_
+        )
+        l_self_modules_features_modules_5_modules_10_modules_attn_parameters_relative_position_bias_table_ = L_self_modules_features_modules_5_modules_10_modules_attn_parameters_relative_position_bias_table_
+        l_self_modules_features_modules_5_modules_10_modules_attn_buffers_relative_position_index_ = L_self_modules_features_modules_5_modules_10_modules_attn_buffers_relative_position_index_
+        l_self_modules_features_modules_5_modules_10_modules_attn_modules_qkv_parameters_weight_ = L_self_modules_features_modules_5_modules_10_modules_attn_modules_qkv_parameters_weight_
+        l_self_modules_features_modules_5_modules_10_modules_attn_modules_proj_parameters_weight_ = L_self_modules_features_modules_5_modules_10_modules_attn_modules_proj_parameters_weight_
+        l_self_modules_features_modules_5_modules_10_modules_attn_modules_qkv_parameters_bias_ = L_self_modules_features_modules_5_modules_10_modules_attn_modules_qkv_parameters_bias_
+        l_self_modules_features_modules_5_modules_10_modules_attn_modules_proj_parameters_bias_ = L_self_modules_features_modules_5_modules_10_modules_attn_modules_proj_parameters_bias_
+        l_self_modules_features_modules_5_modules_10_modules_norm2_parameters_weight_ = L_self_modules_features_modules_5_modules_10_modules_norm2_parameters_weight_
+        l_self_modules_features_modules_5_modules_10_modules_norm2_parameters_bias_ = (
+            L_self_modules_features_modules_5_modules_10_modules_norm2_parameters_bias_
+        )
+        l_self_modules_features_modules_5_modules_10_modules_mlp_modules_0_parameters_weight_ = L_self_modules_features_modules_5_modules_10_modules_mlp_modules_0_parameters_weight_
+        l_self_modules_features_modules_5_modules_10_modules_mlp_modules_0_parameters_bias_ = L_self_modules_features_modules_5_modules_10_modules_mlp_modules_0_parameters_bias_
+        l_self_modules_features_modules_5_modules_10_modules_mlp_modules_3_parameters_weight_ = L_self_modules_features_modules_5_modules_10_modules_mlp_modules_3_parameters_weight_
+        l_self_modules_features_modules_5_modules_10_modules_mlp_modules_3_parameters_bias_ = L_self_modules_features_modules_5_modules_10_modules_mlp_modules_3_parameters_bias_
+        l_self_modules_features_modules_5_modules_11_modules_norm1_parameters_weight_ = L_self_modules_features_modules_5_modules_11_modules_norm1_parameters_weight_
+        l_self_modules_features_modules_5_modules_11_modules_norm1_parameters_bias_ = (
+            L_self_modules_features_modules_5_modules_11_modules_norm1_parameters_bias_
+        )
+        l_self_modules_features_modules_5_modules_11_modules_attn_parameters_relative_position_bias_table_ = L_self_modules_features_modules_5_modules_11_modules_attn_parameters_relative_position_bias_table_
+        l_self_modules_features_modules_5_modules_11_modules_attn_buffers_relative_position_index_ = L_self_modules_features_modules_5_modules_11_modules_attn_buffers_relative_position_index_
+        l_self_modules_features_modules_5_modules_11_modules_attn_modules_qkv_parameters_weight_ = L_self_modules_features_modules_5_modules_11_modules_attn_modules_qkv_parameters_weight_
+        l_self_modules_features_modules_5_modules_11_modules_attn_modules_proj_parameters_weight_ = L_self_modules_features_modules_5_modules_11_modules_attn_modules_proj_parameters_weight_
+        l_self_modules_features_modules_5_modules_11_modules_attn_modules_qkv_parameters_bias_ = L_self_modules_features_modules_5_modules_11_modules_attn_modules_qkv_parameters_bias_
+        l_self_modules_features_modules_5_modules_11_modules_attn_modules_proj_parameters_bias_ = L_self_modules_features_modules_5_modules_11_modules_attn_modules_proj_parameters_bias_
+        l_self_modules_features_modules_5_modules_11_modules_norm2_parameters_weight_ = L_self_modules_features_modules_5_modules_11_modules_norm2_parameters_weight_
+        l_self_modules_features_modules_5_modules_11_modules_norm2_parameters_bias_ = (
+            L_self_modules_features_modules_5_modules_11_modules_norm2_parameters_bias_
+        )
+        l_self_modules_features_modules_5_modules_11_modules_mlp_modules_0_parameters_weight_ = L_self_modules_features_modules_5_modules_11_modules_mlp_modules_0_parameters_weight_
+        l_self_modules_features_modules_5_modules_11_modules_mlp_modules_0_parameters_bias_ = L_self_modules_features_modules_5_modules_11_modules_mlp_modules_0_parameters_bias_
+        l_self_modules_features_modules_5_modules_11_modules_mlp_modules_3_parameters_weight_ = L_self_modules_features_modules_5_modules_11_modules_mlp_modules_3_parameters_weight_
+        l_self_modules_features_modules_5_modules_11_modules_mlp_modules_3_parameters_bias_ = L_self_modules_features_modules_5_modules_11_modules_mlp_modules_3_parameters_bias_
+        l_self_modules_features_modules_5_modules_12_modules_norm1_parameters_weight_ = L_self_modules_features_modules_5_modules_12_modules_norm1_parameters_weight_
+        l_self_modules_features_modules_5_modules_12_modules_norm1_parameters_bias_ = (
+            L_self_modules_features_modules_5_modules_12_modules_norm1_parameters_bias_
+        )
+        l_self_modules_features_modules_5_modules_12_modules_attn_parameters_relative_position_bias_table_ = L_self_modules_features_modules_5_modules_12_modules_attn_parameters_relative_position_bias_table_
+        l_self_modules_features_modules_5_modules_12_modules_attn_buffers_relative_position_index_ = L_self_modules_features_modules_5_modules_12_modules_attn_buffers_relative_position_index_
+        l_self_modules_features_modules_5_modules_12_modules_attn_modules_qkv_parameters_weight_ = L_self_modules_features_modules_5_modules_12_modules_attn_modules_qkv_parameters_weight_
+        l_self_modules_features_modules_5_modules_12_modules_attn_modules_proj_parameters_weight_ = L_self_modules_features_modules_5_modules_12_modules_attn_modules_proj_parameters_weight_
+        l_self_modules_features_modules_5_modules_12_modules_attn_modules_qkv_parameters_bias_ = L_self_modules_features_modules_5_modules_12_modules_attn_modules_qkv_parameters_bias_
+        l_self_modules_features_modules_5_modules_12_modules_attn_modules_proj_parameters_bias_ = L_self_modules_features_modules_5_modules_12_modules_attn_modules_proj_parameters_bias_
+        l_self_modules_features_modules_5_modules_12_modules_norm2_parameters_weight_ = L_self_modules_features_modules_5_modules_12_modules_norm2_parameters_weight_
+        l_self_modules_features_modules_5_modules_12_modules_norm2_parameters_bias_ = (
+            L_self_modules_features_modules_5_modules_12_modules_norm2_parameters_bias_
+        )
+        l_self_modules_features_modules_5_modules_12_modules_mlp_modules_0_parameters_weight_ = L_self_modules_features_modules_5_modules_12_modules_mlp_modules_0_parameters_weight_
+        l_self_modules_features_modules_5_modules_12_modules_mlp_modules_0_parameters_bias_ = L_self_modules_features_modules_5_modules_12_modules_mlp_modules_0_parameters_bias_
+        l_self_modules_features_modules_5_modules_12_modules_mlp_modules_3_parameters_weight_ = L_self_modules_features_modules_5_modules_12_modules_mlp_modules_3_parameters_weight_
+        l_self_modules_features_modules_5_modules_12_modules_mlp_modules_3_parameters_bias_ = L_self_modules_features_modules_5_modules_12_modules_mlp_modules_3_parameters_bias_
+        l_self_modules_features_modules_5_modules_13_modules_norm1_parameters_weight_ = L_self_modules_features_modules_5_modules_13_modules_norm1_parameters_weight_
+        l_self_modules_features_modules_5_modules_13_modules_norm1_parameters_bias_ = (
+            L_self_modules_features_modules_5_modules_13_modules_norm1_parameters_bias_
+        )
+        l_self_modules_features_modules_5_modules_13_modules_attn_parameters_relative_position_bias_table_ = L_self_modules_features_modules_5_modules_13_modules_attn_parameters_relative_position_bias_table_
+        l_self_modules_features_modules_5_modules_13_modules_attn_buffers_relative_position_index_ = L_self_modules_features_modules_5_modules_13_modules_attn_buffers_relative_position_index_
+        l_self_modules_features_modules_5_modules_13_modules_attn_modules_qkv_parameters_weight_ = L_self_modules_features_modules_5_modules_13_modules_attn_modules_qkv_parameters_weight_
+        l_self_modules_features_modules_5_modules_13_modules_attn_modules_proj_parameters_weight_ = L_self_modules_features_modules_5_modules_13_modules_attn_modules_proj_parameters_weight_
+        l_self_modules_features_modules_5_modules_13_modules_attn_modules_qkv_parameters_bias_ = L_self_modules_features_modules_5_modules_13_modules_attn_modules_qkv_parameters_bias_
+        l_self_modules_features_modules_5_modules_13_modules_attn_modules_proj_parameters_bias_ = L_self_modules_features_modules_5_modules_13_modules_attn_modules_proj_parameters_bias_
+        l_self_modules_features_modules_5_modules_13_modules_norm2_parameters_weight_ = L_self_modules_features_modules_5_modules_13_modules_norm2_parameters_weight_
+        l_self_modules_features_modules_5_modules_13_modules_norm2_parameters_bias_ = (
+            L_self_modules_features_modules_5_modules_13_modules_norm2_parameters_bias_
+        )
+        l_self_modules_features_modules_5_modules_13_modules_mlp_modules_0_parameters_weight_ = L_self_modules_features_modules_5_modules_13_modules_mlp_modules_0_parameters_weight_
+        l_self_modules_features_modules_5_modules_13_modules_mlp_modules_0_parameters_bias_ = L_self_modules_features_modules_5_modules_13_modules_mlp_modules_0_parameters_bias_
+        l_self_modules_features_modules_5_modules_13_modules_mlp_modules_3_parameters_weight_ = L_self_modules_features_modules_5_modules_13_modules_mlp_modules_3_parameters_weight_
+        l_self_modules_features_modules_5_modules_13_modules_mlp_modules_3_parameters_bias_ = L_self_modules_features_modules_5_modules_13_modules_mlp_modules_3_parameters_bias_
+        l_self_modules_features_modules_5_modules_14_modules_norm1_parameters_weight_ = L_self_modules_features_modules_5_modules_14_modules_norm1_parameters_weight_
+        l_self_modules_features_modules_5_modules_14_modules_norm1_parameters_bias_ = (
+            L_self_modules_features_modules_5_modules_14_modules_norm1_parameters_bias_
+        )
+        l_self_modules_features_modules_5_modules_14_modules_attn_parameters_relative_position_bias_table_ = L_self_modules_features_modules_5_modules_14_modules_attn_parameters_relative_position_bias_table_
+        l_self_modules_features_modules_5_modules_14_modules_attn_buffers_relative_position_index_ = L_self_modules_features_modules_5_modules_14_modules_attn_buffers_relative_position_index_
+        l_self_modules_features_modules_5_modules_14_modules_attn_modules_qkv_parameters_weight_ = L_self_modules_features_modules_5_modules_14_modules_attn_modules_qkv_parameters_weight_
+        l_self_modules_features_modules_5_modules_14_modules_attn_modules_proj_parameters_weight_ = L_self_modules_features_modules_5_modules_14_modules_attn_modules_proj_parameters_weight_
+        l_self_modules_features_modules_5_modules_14_modules_attn_modules_qkv_parameters_bias_ = L_self_modules_features_modules_5_modules_14_modules_attn_modules_qkv_parameters_bias_
+        l_self_modules_features_modules_5_modules_14_modules_attn_modules_proj_parameters_bias_ = L_self_modules_features_modules_5_modules_14_modules_attn_modules_proj_parameters_bias_
+        l_self_modules_features_modules_5_modules_14_modules_norm2_parameters_weight_ = L_self_modules_features_modules_5_modules_14_modules_norm2_parameters_weight_
+        l_self_modules_features_modules_5_modules_14_modules_norm2_parameters_bias_ = (
+            L_self_modules_features_modules_5_modules_14_modules_norm2_parameters_bias_
+        )
+        l_self_modules_features_modules_5_modules_14_modules_mlp_modules_0_parameters_weight_ = L_self_modules_features_modules_5_modules_14_modules_mlp_modules_0_parameters_weight_
+        l_self_modules_features_modules_5_modules_14_modules_mlp_modules_0_parameters_bias_ = L_self_modules_features_modules_5_modules_14_modules_mlp_modules_0_parameters_bias_
+        l_self_modules_features_modules_5_modules_14_modules_mlp_modules_3_parameters_weight_ = L_self_modules_features_modules_5_modules_14_modules_mlp_modules_3_parameters_weight_
+        l_self_modules_features_modules_5_modules_14_modules_mlp_modules_3_parameters_bias_ = L_self_modules_features_modules_5_modules_14_modules_mlp_modules_3_parameters_bias_
+        l_self_modules_features_modules_5_modules_15_modules_norm1_parameters_weight_ = L_self_modules_features_modules_5_modules_15_modules_norm1_parameters_weight_
+        l_self_modules_features_modules_5_modules_15_modules_norm1_parameters_bias_ = (
+            L_self_modules_features_modules_5_modules_15_modules_norm1_parameters_bias_
+        )
+        l_self_modules_features_modules_5_modules_15_modules_attn_parameters_relative_position_bias_table_ = L_self_modules_features_modules_5_modules_15_modules_attn_parameters_relative_position_bias_table_
+        l_self_modules_features_modules_5_modules_15_modules_attn_buffers_relative_position_index_ = L_self_modules_features_modules_5_modules_15_modules_attn_buffers_relative_position_index_
+        l_self_modules_features_modules_5_modules_15_modules_attn_modules_qkv_parameters_weight_ = L_self_modules_features_modules_5_modules_15_modules_attn_modules_qkv_parameters_weight_
+        l_self_modules_features_modules_5_modules_15_modules_attn_modules_proj_parameters_weight_ = L_self_modules_features_modules_5_modules_15_modules_attn_modules_proj_parameters_weight_
+        l_self_modules_features_modules_5_modules_15_modules_attn_modules_qkv_parameters_bias_ = L_self_modules_features_modules_5_modules_15_modules_attn_modules_qkv_parameters_bias_
+        l_self_modules_features_modules_5_modules_15_modules_attn_modules_proj_parameters_bias_ = L_self_modules_features_modules_5_modules_15_modules_attn_modules_proj_parameters_bias_
+        l_self_modules_features_modules_5_modules_15_modules_norm2_parameters_weight_ = L_self_modules_features_modules_5_modules_15_modules_norm2_parameters_weight_
+        l_self_modules_features_modules_5_modules_15_modules_norm2_parameters_bias_ = (
+            L_self_modules_features_modules_5_modules_15_modules_norm2_parameters_bias_
+        )
+        l_self_modules_features_modules_5_modules_15_modules_mlp_modules_0_parameters_weight_ = L_self_modules_features_modules_5_modules_15_modules_mlp_modules_0_parameters_weight_
+        l_self_modules_features_modules_5_modules_15_modules_mlp_modules_0_parameters_bias_ = L_self_modules_features_modules_5_modules_15_modules_mlp_modules_0_parameters_bias_
+        l_self_modules_features_modules_5_modules_15_modules_mlp_modules_3_parameters_weight_ = L_self_modules_features_modules_5_modules_15_modules_mlp_modules_3_parameters_weight_
+        l_self_modules_features_modules_5_modules_15_modules_mlp_modules_3_parameters_bias_ = L_self_modules_features_modules_5_modules_15_modules_mlp_modules_3_parameters_bias_
+        l_self_modules_features_modules_5_modules_16_modules_norm1_parameters_weight_ = L_self_modules_features_modules_5_modules_16_modules_norm1_parameters_weight_
+        l_self_modules_features_modules_5_modules_16_modules_norm1_parameters_bias_ = (
+            L_self_modules_features_modules_5_modules_16_modules_norm1_parameters_bias_
+        )
+        l_self_modules_features_modules_5_modules_16_modules_attn_parameters_relative_position_bias_table_ = L_self_modules_features_modules_5_modules_16_modules_attn_parameters_relative_position_bias_table_
+        l_self_modules_features_modules_5_modules_16_modules_attn_buffers_relative_position_index_ = L_self_modules_features_modules_5_modules_16_modules_attn_buffers_relative_position_index_
+        l_self_modules_features_modules_5_modules_16_modules_attn_modules_qkv_parameters_weight_ = L_self_modules_features_modules_5_modules_16_modules_attn_modules_qkv_parameters_weight_
+        l_self_modules_features_modules_5_modules_16_modules_attn_modules_proj_parameters_weight_ = L_self_modules_features_modules_5_modules_16_modules_attn_modules_proj_parameters_weight_
+        l_self_modules_features_modules_5_modules_16_modules_attn_modules_qkv_parameters_bias_ = L_self_modules_features_modules_5_modules_16_modules_attn_modules_qkv_parameters_bias_
+        l_self_modules_features_modules_5_modules_16_modules_attn_modules_proj_parameters_bias_ = L_self_modules_features_modules_5_modules_16_modules_attn_modules_proj_parameters_bias_
+        l_self_modules_features_modules_5_modules_16_modules_norm2_parameters_weight_ = L_self_modules_features_modules_5_modules_16_modules_norm2_parameters_weight_
+        l_self_modules_features_modules_5_modules_16_modules_norm2_parameters_bias_ = (
+            L_self_modules_features_modules_5_modules_16_modules_norm2_parameters_bias_
+        )
+        l_self_modules_features_modules_5_modules_16_modules_mlp_modules_0_parameters_weight_ = L_self_modules_features_modules_5_modules_16_modules_mlp_modules_0_parameters_weight_
+        l_self_modules_features_modules_5_modules_16_modules_mlp_modules_0_parameters_bias_ = L_self_modules_features_modules_5_modules_16_modules_mlp_modules_0_parameters_bias_
+        l_self_modules_features_modules_5_modules_16_modules_mlp_modules_3_parameters_weight_ = L_self_modules_features_modules_5_modules_16_modules_mlp_modules_3_parameters_weight_
+        l_self_modules_features_modules_5_modules_16_modules_mlp_modules_3_parameters_bias_ = L_self_modules_features_modules_5_modules_16_modules_mlp_modules_3_parameters_bias_
+        l_self_modules_features_modules_5_modules_17_modules_norm1_parameters_weight_ = L_self_modules_features_modules_5_modules_17_modules_norm1_parameters_weight_
+        l_self_modules_features_modules_5_modules_17_modules_norm1_parameters_bias_ = (
+            L_self_modules_features_modules_5_modules_17_modules_norm1_parameters_bias_
+        )
+        l_self_modules_features_modules_5_modules_17_modules_attn_parameters_relative_position_bias_table_ = L_self_modules_features_modules_5_modules_17_modules_attn_parameters_relative_position_bias_table_
+        l_self_modules_features_modules_5_modules_17_modules_attn_buffers_relative_position_index_ = L_self_modules_features_modules_5_modules_17_modules_attn_buffers_relative_position_index_
+        l_self_modules_features_modules_5_modules_17_modules_attn_modules_qkv_parameters_weight_ = L_self_modules_features_modules_5_modules_17_modules_attn_modules_qkv_parameters_weight_
+        l_self_modules_features_modules_5_modules_17_modules_attn_modules_proj_parameters_weight_ = L_self_modules_features_modules_5_modules_17_modules_attn_modules_proj_parameters_weight_
+        l_self_modules_features_modules_5_modules_17_modules_attn_modules_qkv_parameters_bias_ = L_self_modules_features_modules_5_modules_17_modules_attn_modules_qkv_parameters_bias_
+        l_self_modules_features_modules_5_modules_17_modules_attn_modules_proj_parameters_bias_ = L_self_modules_features_modules_5_modules_17_modules_attn_modules_proj_parameters_bias_
+        l_self_modules_features_modules_5_modules_17_modules_norm2_parameters_weight_ = L_self_modules_features_modules_5_modules_17_modules_norm2_parameters_weight_
+        l_self_modules_features_modules_5_modules_17_modules_norm2_parameters_bias_ = (
+            L_self_modules_features_modules_5_modules_17_modules_norm2_parameters_bias_
+        )
+        l_self_modules_features_modules_5_modules_17_modules_mlp_modules_0_parameters_weight_ = L_self_modules_features_modules_5_modules_17_modules_mlp_modules_0_parameters_weight_
+        l_self_modules_features_modules_5_modules_17_modules_mlp_modules_0_parameters_bias_ = L_self_modules_features_modules_5_modules_17_modules_mlp_modules_0_parameters_bias_
+        l_self_modules_features_modules_5_modules_17_modules_mlp_modules_3_parameters_weight_ = L_self_modules_features_modules_5_modules_17_modules_mlp_modules_3_parameters_weight_
+        l_self_modules_features_modules_5_modules_17_modules_mlp_modules_3_parameters_bias_ = L_self_modules_features_modules_5_modules_17_modules_mlp_modules_3_parameters_bias_
+        l_self_modules_features_modules_6_modules_norm_parameters_weight_ = (
+            L_self_modules_features_modules_6_modules_norm_parameters_weight_
+        )
+        l_self_modules_features_modules_6_modules_norm_parameters_bias_ = (
+            L_self_modules_features_modules_6_modules_norm_parameters_bias_
+        )
+        l_self_modules_features_modules_6_modules_reduction_parameters_weight_ = (
+            L_self_modules_features_modules_6_modules_reduction_parameters_weight_
+        )
+        l_self_modules_features_modules_7_modules_0_modules_norm1_parameters_weight_ = (
+            L_self_modules_features_modules_7_modules_0_modules_norm1_parameters_weight_
+        )
+        l_self_modules_features_modules_7_modules_0_modules_norm1_parameters_bias_ = (
+            L_self_modules_features_modules_7_modules_0_modules_norm1_parameters_bias_
+        )
+        l_self_modules_features_modules_7_modules_0_modules_attn_parameters_relative_position_bias_table_ = L_self_modules_features_modules_7_modules_0_modules_attn_parameters_relative_position_bias_table_
+        l_self_modules_features_modules_7_modules_0_modules_attn_buffers_relative_position_index_ = L_self_modules_features_modules_7_modules_0_modules_attn_buffers_relative_position_index_
+        l_self_modules_features_modules_7_modules_0_modules_attn_modules_qkv_parameters_weight_ = L_self_modules_features_modules_7_modules_0_modules_attn_modules_qkv_parameters_weight_
+        l_self_modules_features_modules_7_modules_0_modules_attn_modules_proj_parameters_weight_ = L_self_modules_features_modules_7_modules_0_modules_attn_modules_proj_parameters_weight_
+        l_self_modules_features_modules_7_modules_0_modules_attn_modules_qkv_parameters_bias_ = L_self_modules_features_modules_7_modules_0_modules_attn_modules_qkv_parameters_bias_
+        l_self_modules_features_modules_7_modules_0_modules_attn_modules_proj_parameters_bias_ = L_self_modules_features_modules_7_modules_0_modules_attn_modules_proj_parameters_bias_
+        l_self_modules_features_modules_7_modules_0_modules_norm2_parameters_weight_ = (
+            L_self_modules_features_modules_7_modules_0_modules_norm2_parameters_weight_
+        )
+        l_self_modules_features_modules_7_modules_0_modules_norm2_parameters_bias_ = (
+            L_self_modules_features_modules_7_modules_0_modules_norm2_parameters_bias_
+        )
+        l_self_modules_features_modules_7_modules_0_modules_mlp_modules_0_parameters_weight_ = L_self_modules_features_modules_7_modules_0_modules_mlp_modules_0_parameters_weight_
+        l_self_modules_features_modules_7_modules_0_modules_mlp_modules_0_parameters_bias_ = L_self_modules_features_modules_7_modules_0_modules_mlp_modules_0_parameters_bias_
+        l_self_modules_features_modules_7_modules_0_modules_mlp_modules_3_parameters_weight_ = L_self_modules_features_modules_7_modules_0_modules_mlp_modules_3_parameters_weight_
+        l_self_modules_features_modules_7_modules_0_modules_mlp_modules_3_parameters_bias_ = L_self_modules_features_modules_7_modules_0_modules_mlp_modules_3_parameters_bias_
+        l_self_modules_features_modules_7_modules_1_modules_norm1_parameters_weight_ = (
+            L_self_modules_features_modules_7_modules_1_modules_norm1_parameters_weight_
+        )
+        l_self_modules_features_modules_7_modules_1_modules_norm1_parameters_bias_ = (
+            L_self_modules_features_modules_7_modules_1_modules_norm1_parameters_bias_
+        )
+        l_self_modules_features_modules_7_modules_1_modules_attn_parameters_relative_position_bias_table_ = L_self_modules_features_modules_7_modules_1_modules_attn_parameters_relative_position_bias_table_
+        l_self_modules_features_modules_7_modules_1_modules_attn_buffers_relative_position_index_ = L_self_modules_features_modules_7_modules_1_modules_attn_buffers_relative_position_index_
+        l_self_modules_features_modules_7_modules_1_modules_attn_modules_qkv_parameters_weight_ = L_self_modules_features_modules_7_modules_1_modules_attn_modules_qkv_parameters_weight_
+        l_self_modules_features_modules_7_modules_1_modules_attn_modules_proj_parameters_weight_ = L_self_modules_features_modules_7_modules_1_modules_attn_modules_proj_parameters_weight_
+        l_self_modules_features_modules_7_modules_1_modules_attn_modules_qkv_parameters_bias_ = L_self_modules_features_modules_7_modules_1_modules_attn_modules_qkv_parameters_bias_
+        l_self_modules_features_modules_7_modules_1_modules_attn_modules_proj_parameters_bias_ = L_self_modules_features_modules_7_modules_1_modules_attn_modules_proj_parameters_bias_
+        l_self_modules_features_modules_7_modules_1_modules_norm2_parameters_weight_ = (
+            L_self_modules_features_modules_7_modules_1_modules_norm2_parameters_weight_
+        )
+        l_self_modules_features_modules_7_modules_1_modules_norm2_parameters_bias_ = (
+            L_self_modules_features_modules_7_modules_1_modules_norm2_parameters_bias_
+        )
+        l_self_modules_features_modules_7_modules_1_modules_mlp_modules_0_parameters_weight_ = L_self_modules_features_modules_7_modules_1_modules_mlp_modules_0_parameters_weight_
+        l_self_modules_features_modules_7_modules_1_modules_mlp_modules_0_parameters_bias_ = L_self_modules_features_modules_7_modules_1_modules_mlp_modules_0_parameters_bias_
+        l_self_modules_features_modules_7_modules_1_modules_mlp_modules_3_parameters_weight_ = L_self_modules_features_modules_7_modules_1_modules_mlp_modules_3_parameters_weight_
+        l_self_modules_features_modules_7_modules_1_modules_mlp_modules_3_parameters_bias_ = L_self_modules_features_modules_7_modules_1_modules_mlp_modules_3_parameters_bias_
+        l_self_modules_norm_parameters_weight_ = L_self_modules_norm_parameters_weight_
+        l_self_modules_norm_parameters_bias_ = L_self_modules_norm_parameters_bias_
+        l_self_modules_head_parameters_weight_ = L_self_modules_head_parameters_weight_
+        l_self_modules_head_parameters_bias_ = L_self_modules_head_parameters_bias_
+        input_1 = torch.conv2d(
+            l_x_,
+            l_self_modules_features_modules_0_modules_0_parameters_weight_,
+            l_self_modules_features_modules_0_modules_0_parameters_bias_,
+            (4, 4),
+            (0, 0),
+            (1, 1),
+            1,
+        )
+        l_x_ = (
+            l_self_modules_features_modules_0_modules_0_parameters_weight_
+        ) = l_self_modules_features_modules_0_modules_0_parameters_bias_ = None
+        input_2 = torch.permute(input_1, [0, 2, 3, 1])
+        input_1 = None
+        input_3 = torch.nn.functional.layer_norm(
+            input_2,
+            (128,),
+            l_self_modules_features_modules_0_modules_2_parameters_weight_,
+            l_self_modules_features_modules_0_modules_2_parameters_bias_,
+            1e-05,
+        )
+        input_2 = (
+            l_self_modules_features_modules_0_modules_2_parameters_weight_
+        ) = l_self_modules_features_modules_0_modules_2_parameters_bias_ = None
+        layer_norm_1 = torch.nn.functional.layer_norm(
+            input_3,
+            (128,),
+            l_self_modules_features_modules_1_modules_0_modules_norm1_parameters_weight_,
+            l_self_modules_features_modules_1_modules_0_modules_norm1_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_1_modules_0_modules_norm1_parameters_weight_ = (
+            l_self_modules_features_modules_1_modules_0_modules_norm1_parameters_bias_
+        ) = None
+        relative_position_bias = l_self_modules_features_modules_1_modules_0_modules_attn_parameters_relative_position_bias_table_[
+            l_self_modules_features_modules_1_modules_0_modules_attn_buffers_relative_position_index_
+        ]
+        l_self_modules_features_modules_1_modules_0_modules_attn_parameters_relative_position_bias_table_ = l_self_modules_features_modules_1_modules_0_modules_attn_buffers_relative_position_index_ = (None)
+        relative_position_bias_1 = relative_position_bias.view(49, 49, -1)
+        relative_position_bias = None
+        permute_1 = relative_position_bias_1.permute(2, 0, 1)
+        relative_position_bias_1 = None
+        contiguous = permute_1.contiguous()
+        permute_1 = None
+        relative_position_bias_2 = contiguous.unsqueeze(0)
+        contiguous = None
+        x = torch._C._nn.pad(layer_norm_1, (0, 0, 0, 0, 0, 0), "constant", None)
+        layer_norm_1 = None
+        x_1 = x.view(1, 8, 7, 8, 7, 128)
+        x = None
+        permute_2 = x_1.permute(0, 1, 3, 2, 4, 5)
+        x_1 = None
+        x_2 = permute_2.reshape(64, 49, 128)
+        permute_2 = None
+        qkv = torch._C._nn.linear(
+            x_2,
+            l_self_modules_features_modules_1_modules_0_modules_attn_modules_qkv_parameters_weight_,
+            l_self_modules_features_modules_1_modules_0_modules_attn_modules_qkv_parameters_bias_,
+        )
+        x_2 = l_self_modules_features_modules_1_modules_0_modules_attn_modules_qkv_parameters_weight_ = l_self_modules_features_modules_1_modules_0_modules_attn_modules_qkv_parameters_bias_ = (None)
+        reshape_1 = qkv.reshape(64, 49, 3, 4, 32)
+        qkv = None
+        qkv_1 = reshape_1.permute(2, 0, 3, 1, 4)
+        reshape_1 = None
+        q = qkv_1[0]
+        k = qkv_1[1]
+        v = qkv_1[2]
+        qkv_1 = None
+        q_1 = q * 0.1767766952966369
+        q = None
+        transpose = k.transpose(-2, -1)
+        k = None
+        attn = q_1.matmul(transpose)
+        q_1 = transpose = None
+        attn_1 = attn + relative_position_bias_2
+        attn = relative_position_bias_2 = None
+        attn_2 = torch.nn.functional.softmax(attn_1, dim=-1)
+        attn_1 = None
+        attn_3 = torch.nn.functional.dropout(attn_2, p=0.0, training=False)
+        attn_2 = None
+        matmul_1 = attn_3.matmul(v)
+        attn_3 = v = None
+        transpose_1 = matmul_1.transpose(1, 2)
+        matmul_1 = None
+        x_3 = transpose_1.reshape(64, 49, 128)
+        transpose_1 = None
+        x_4 = torch._C._nn.linear(
+            x_3,
+            l_self_modules_features_modules_1_modules_0_modules_attn_modules_proj_parameters_weight_,
+            l_self_modules_features_modules_1_modules_0_modules_attn_modules_proj_parameters_bias_,
+        )
+        x_3 = l_self_modules_features_modules_1_modules_0_modules_attn_modules_proj_parameters_weight_ = l_self_modules_features_modules_1_modules_0_modules_attn_modules_proj_parameters_bias_ = (None)
+        x_5 = torch.nn.functional.dropout(x_4, p=0.0, training=False)
+        x_4 = None
+        x_6 = x_5.view(1, 8, 8, 7, 7, 128)
+        x_5 = None
+        permute_4 = x_6.permute(0, 1, 3, 2, 4, 5)
+        x_6 = None
+        x_7 = permute_4.reshape(1, 56, 56, 128)
+        permute_4 = None
+        getitem_4 = x_7[
+            (
+                slice(None, None, None),
+                slice(None, 56, None),
+                slice(None, 56, None),
+                slice(None, None, None),
+            )
+        ]
+        x_7 = None
+        x_8 = getitem_4.contiguous()
+        getitem_4 = None
+        _log_api_usage_once = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once = None
+        x_9 = input_3 + x_8
+        input_3 = x_8 = None
+        layer_norm_2 = torch.nn.functional.layer_norm(
+            x_9,
+            (128,),
+            l_self_modules_features_modules_1_modules_0_modules_norm2_parameters_weight_,
+            l_self_modules_features_modules_1_modules_0_modules_norm2_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_1_modules_0_modules_norm2_parameters_weight_ = (
+            l_self_modules_features_modules_1_modules_0_modules_norm2_parameters_bias_
+        ) = None
+        input_4 = torch._C._nn.linear(
+            layer_norm_2,
+            l_self_modules_features_modules_1_modules_0_modules_mlp_modules_0_parameters_weight_,
+            l_self_modules_features_modules_1_modules_0_modules_mlp_modules_0_parameters_bias_,
+        )
+        layer_norm_2 = l_self_modules_features_modules_1_modules_0_modules_mlp_modules_0_parameters_weight_ = l_self_modules_features_modules_1_modules_0_modules_mlp_modules_0_parameters_bias_ = (None)
+        input_5 = torch._C._nn.gelu(input_4, approximate="none")
+        input_4 = None
+        input_6 = torch.nn.functional.dropout(input_5, 0.0, False, False)
+        input_5 = None
+        input_7 = torch._C._nn.linear(
+            input_6,
+            l_self_modules_features_modules_1_modules_0_modules_mlp_modules_3_parameters_weight_,
+            l_self_modules_features_modules_1_modules_0_modules_mlp_modules_3_parameters_bias_,
+        )
+        input_6 = l_self_modules_features_modules_1_modules_0_modules_mlp_modules_3_parameters_weight_ = l_self_modules_features_modules_1_modules_0_modules_mlp_modules_3_parameters_bias_ = (None)
+        input_8 = torch.nn.functional.dropout(input_7, 0.0, False, False)
+        input_7 = None
+        _log_api_usage_once_1 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_1 = None
+        x_10 = x_9 + input_8
+        x_9 = input_8 = None
+        layer_norm_3 = torch.nn.functional.layer_norm(
+            x_10,
+            (128,),
+            l_self_modules_features_modules_1_modules_1_modules_norm1_parameters_weight_,
+            l_self_modules_features_modules_1_modules_1_modules_norm1_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_1_modules_1_modules_norm1_parameters_weight_ = (
+            l_self_modules_features_modules_1_modules_1_modules_norm1_parameters_bias_
+        ) = None
+        relative_position_bias_3 = l_self_modules_features_modules_1_modules_1_modules_attn_parameters_relative_position_bias_table_[
+            l_self_modules_features_modules_1_modules_1_modules_attn_buffers_relative_position_index_
+        ]
+        l_self_modules_features_modules_1_modules_1_modules_attn_parameters_relative_position_bias_table_ = l_self_modules_features_modules_1_modules_1_modules_attn_buffers_relative_position_index_ = (None)
+        relative_position_bias_4 = relative_position_bias_3.view(49, 49, -1)
+        relative_position_bias_3 = None
+        permute_5 = relative_position_bias_4.permute(2, 0, 1)
+        relative_position_bias_4 = None
+        contiguous_2 = permute_5.contiguous()
+        permute_5 = None
+        relative_position_bias_5 = contiguous_2.unsqueeze(0)
+        contiguous_2 = None
+        x_11 = torch._C._nn.pad(layer_norm_3, (0, 0, 0, 0, 0, 0), "constant", None)
+        layer_norm_3 = None
+        x_12 = torch.roll(x_11, shifts=(-3, -3), dims=(1, 2))
+        x_11 = None
+        x_13 = x_12.view(1, 8, 7, 8, 7, 128)
+        x_12 = None
+        permute_6 = x_13.permute(0, 1, 3, 2, 4, 5)
+        x_13 = None
+        x_14 = permute_6.reshape(64, 49, 128)
+        permute_6 = None
+        qkv_2 = torch._C._nn.linear(
+            x_14,
+            l_self_modules_features_modules_1_modules_1_modules_attn_modules_qkv_parameters_weight_,
+            l_self_modules_features_modules_1_modules_1_modules_attn_modules_qkv_parameters_bias_,
+        )
+        l_self_modules_features_modules_1_modules_1_modules_attn_modules_qkv_parameters_weight_ = l_self_modules_features_modules_1_modules_1_modules_attn_modules_qkv_parameters_bias_ = (None)
+        reshape_5 = qkv_2.reshape(64, 49, 3, 4, 32)
+        qkv_2 = None
+        qkv_3 = reshape_5.permute(2, 0, 3, 1, 4)
+        reshape_5 = None
+        q_2 = qkv_3[0]
+        k_1 = qkv_3[1]
+        v_1 = qkv_3[2]
+        qkv_3 = None
+        q_3 = q_2 * 0.1767766952966369
+        q_2 = None
+        transpose_2 = k_1.transpose(-2, -1)
+        k_1 = None
+        attn_4 = q_3.matmul(transpose_2)
+        q_3 = transpose_2 = None
+        attn_5 = attn_4 + relative_position_bias_5
+        attn_4 = relative_position_bias_5 = None
+        attn_mask = x_14.new_zeros((56, 56))
+        x_14 = None
+        attn_mask[(slice(0, -7, None), slice(0, -7, None))] = 0
+        setitem = attn_mask
+        setitem = None
+        attn_mask[(slice(0, -7, None), slice(-7, -3, None))] = 1
+        setitem_1 = attn_mask
+        setitem_1 = None
+        attn_mask[(slice(0, -7, None), slice(-3, None, None))] = 2
+        setitem_2 = attn_mask
+        setitem_2 = None
+        attn_mask[(slice(-7, -3, None), slice(0, -7, None))] = 3
+        setitem_3 = attn_mask
+        setitem_3 = None
+        attn_mask[(slice(-7, -3, None), slice(-7, -3, None))] = 4
+        setitem_4 = attn_mask
+        setitem_4 = None
+        attn_mask[(slice(-7, -3, None), slice(-3, None, None))] = 5
+        setitem_5 = attn_mask
+        setitem_5 = None
+        attn_mask[(slice(-3, None, None), slice(0, -7, None))] = 6
+        setitem_6 = attn_mask
+        setitem_6 = None
+        attn_mask[(slice(-3, None, None), slice(-7, -3, None))] = 7
+        setitem_7 = attn_mask
+        setitem_7 = None
+        attn_mask[(slice(-3, None, None), slice(-3, None, None))] = 8
+        setitem_8 = attn_mask
+        setitem_8 = None
+        attn_mask_1 = attn_mask.view(8, 7, 8, 7)
+        attn_mask = None
+        permute_8 = attn_mask_1.permute(0, 2, 1, 3)
+        attn_mask_1 = None
+        attn_mask_2 = permute_8.reshape(64, 49)
+        permute_8 = None
+        unsqueeze_2 = attn_mask_2.unsqueeze(1)
+        unsqueeze_3 = attn_mask_2.unsqueeze(2)
+        attn_mask_2 = None
+        attn_mask_3 = unsqueeze_2 - unsqueeze_3
+        unsqueeze_2 = unsqueeze_3 = None
+        ne = attn_mask_3 != 0
+        masked_fill = attn_mask_3.masked_fill(ne, -100.0)
+        ne = None
+        eq = attn_mask_3 == 0
+        attn_mask_3 = None
+        attn_mask_4 = masked_fill.masked_fill(eq, 0.0)
+        masked_fill = eq = None
+        attn_6 = attn_5.view(1, 64, 4, 49, 49)
+        attn_5 = None
+        unsqueeze_4 = attn_mask_4.unsqueeze(1)
+        attn_mask_4 = None
+        unsqueeze_5 = unsqueeze_4.unsqueeze(0)
+        unsqueeze_4 = None
+        attn_7 = attn_6 + unsqueeze_5
+        attn_6 = unsqueeze_5 = None
+        attn_8 = attn_7.view(-1, 4, 49, 49)
+        attn_7 = None
+        attn_9 = torch.nn.functional.softmax(attn_8, dim=-1)
+        attn_8 = None
+        attn_10 = torch.nn.functional.dropout(attn_9, p=0.0, training=False)
+        attn_9 = None
+        matmul_3 = attn_10.matmul(v_1)
+        attn_10 = v_1 = None
+        transpose_3 = matmul_3.transpose(1, 2)
+        matmul_3 = None
+        x_15 = transpose_3.reshape(64, 49, 128)
+        transpose_3 = None
+        x_16 = torch._C._nn.linear(
+            x_15,
+            l_self_modules_features_modules_1_modules_1_modules_attn_modules_proj_parameters_weight_,
+            l_self_modules_features_modules_1_modules_1_modules_attn_modules_proj_parameters_bias_,
+        )
+        x_15 = l_self_modules_features_modules_1_modules_1_modules_attn_modules_proj_parameters_weight_ = l_self_modules_features_modules_1_modules_1_modules_attn_modules_proj_parameters_bias_ = (None)
+        x_17 = torch.nn.functional.dropout(x_16, p=0.0, training=False)
+        x_16 = None
+        x_18 = x_17.view(1, 8, 8, 7, 7, 128)
+        x_17 = None
+        permute_9 = x_18.permute(0, 1, 3, 2, 4, 5)
+        x_18 = None
+        x_19 = permute_9.reshape(1, 56, 56, 128)
+        permute_9 = None
+        x_20 = torch.roll(x_19, shifts=(3, 3), dims=(1, 2))
+        x_19 = None
+        getitem_9 = x_20[
+            (
+                slice(None, None, None),
+                slice(None, 56, None),
+                slice(None, 56, None),
+                slice(None, None, None),
+            )
+        ]
+        x_20 = None
+        x_21 = getitem_9.contiguous()
+        getitem_9 = None
+        _log_api_usage_once_2 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_2 = None
+        x_22 = x_10 + x_21
+        x_10 = x_21 = None
+        layer_norm_4 = torch.nn.functional.layer_norm(
+            x_22,
+            (128,),
+            l_self_modules_features_modules_1_modules_1_modules_norm2_parameters_weight_,
+            l_self_modules_features_modules_1_modules_1_modules_norm2_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_1_modules_1_modules_norm2_parameters_weight_ = (
+            l_self_modules_features_modules_1_modules_1_modules_norm2_parameters_bias_
+        ) = None
+        input_9 = torch._C._nn.linear(
+            layer_norm_4,
+            l_self_modules_features_modules_1_modules_1_modules_mlp_modules_0_parameters_weight_,
+            l_self_modules_features_modules_1_modules_1_modules_mlp_modules_0_parameters_bias_,
+        )
+        layer_norm_4 = l_self_modules_features_modules_1_modules_1_modules_mlp_modules_0_parameters_weight_ = l_self_modules_features_modules_1_modules_1_modules_mlp_modules_0_parameters_bias_ = (None)
+        input_10 = torch._C._nn.gelu(input_9, approximate="none")
+        input_9 = None
+        input_11 = torch.nn.functional.dropout(input_10, 0.0, False, False)
+        input_10 = None
+        input_12 = torch._C._nn.linear(
+            input_11,
+            l_self_modules_features_modules_1_modules_1_modules_mlp_modules_3_parameters_weight_,
+            l_self_modules_features_modules_1_modules_1_modules_mlp_modules_3_parameters_bias_,
+        )
+        input_11 = l_self_modules_features_modules_1_modules_1_modules_mlp_modules_3_parameters_weight_ = l_self_modules_features_modules_1_modules_1_modules_mlp_modules_3_parameters_bias_ = (None)
+        input_13 = torch.nn.functional.dropout(input_12, 0.0, False, False)
+        input_12 = None
+        _log_api_usage_once_3 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_3 = None
+        x_23 = x_22 + input_13
+        x_22 = input_13 = None
+        x_24 = torch._C._nn.pad(x_23, (0, 0, 0, 0, 0, 0), "constant", None)
+        x_23 = None
+        x0 = x_24[
+            (Ellipsis, slice(0, None, 2), slice(0, None, 2), slice(None, None, None))
+        ]
+        x1 = x_24[
+            (Ellipsis, slice(1, None, 2), slice(0, None, 2), slice(None, None, None))
+        ]
+        x2 = x_24[
+            (Ellipsis, slice(0, None, 2), slice(1, None, 2), slice(None, None, None))
+        ]
+        x3 = x_24[
+            (Ellipsis, slice(1, None, 2), slice(1, None, 2), slice(None, None, None))
+        ]
+        x_24 = None
+        x_25 = torch.cat([x0, x1, x2, x3], -1)
+        x0 = x1 = x2 = x3 = None
+        x_26 = torch.nn.functional.layer_norm(
+            x_25,
+            (512,),
+            l_self_modules_features_modules_2_modules_norm_parameters_weight_,
+            l_self_modules_features_modules_2_modules_norm_parameters_bias_,
+            1e-05,
+        )
+        x_25 = (
+            l_self_modules_features_modules_2_modules_norm_parameters_weight_
+        ) = l_self_modules_features_modules_2_modules_norm_parameters_bias_ = None
+        x_27 = torch._C._nn.linear(
+            x_26,
+            l_self_modules_features_modules_2_modules_reduction_parameters_weight_,
+            None,
+        )
+        x_26 = (
+            l_self_modules_features_modules_2_modules_reduction_parameters_weight_
+        ) = None
+        layer_norm_6 = torch.nn.functional.layer_norm(
+            x_27,
+            (256,),
+            l_self_modules_features_modules_3_modules_0_modules_norm1_parameters_weight_,
+            l_self_modules_features_modules_3_modules_0_modules_norm1_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_3_modules_0_modules_norm1_parameters_weight_ = (
+            l_self_modules_features_modules_3_modules_0_modules_norm1_parameters_bias_
+        ) = None
+        relative_position_bias_6 = l_self_modules_features_modules_3_modules_0_modules_attn_parameters_relative_position_bias_table_[
+            l_self_modules_features_modules_3_modules_0_modules_attn_buffers_relative_position_index_
+        ]
+        l_self_modules_features_modules_3_modules_0_modules_attn_parameters_relative_position_bias_table_ = l_self_modules_features_modules_3_modules_0_modules_attn_buffers_relative_position_index_ = (None)
+        relative_position_bias_7 = relative_position_bias_6.view(49, 49, -1)
+        relative_position_bias_6 = None
+        permute_10 = relative_position_bias_7.permute(2, 0, 1)
+        relative_position_bias_7 = None
+        contiguous_4 = permute_10.contiguous()
+        permute_10 = None
+        relative_position_bias_8 = contiguous_4.unsqueeze(0)
+        contiguous_4 = None
+        x_28 = torch._C._nn.pad(layer_norm_6, (0, 0, 0, 0, 0, 0), "constant", None)
+        layer_norm_6 = None
+        x_29 = x_28.view(1, 4, 7, 4, 7, 256)
+        x_28 = None
+        permute_11 = x_29.permute(0, 1, 3, 2, 4, 5)
+        x_29 = None
+        x_30 = permute_11.reshape(16, 49, 256)
+        permute_11 = None
+        qkv_4 = torch._C._nn.linear(
+            x_30,
+            l_self_modules_features_modules_3_modules_0_modules_attn_modules_qkv_parameters_weight_,
+            l_self_modules_features_modules_3_modules_0_modules_attn_modules_qkv_parameters_bias_,
+        )
+        x_30 = l_self_modules_features_modules_3_modules_0_modules_attn_modules_qkv_parameters_weight_ = l_self_modules_features_modules_3_modules_0_modules_attn_modules_qkv_parameters_bias_ = (None)
+        reshape_10 = qkv_4.reshape(16, 49, 3, 8, 32)
+        qkv_4 = None
+        qkv_5 = reshape_10.permute(2, 0, 3, 1, 4)
+        reshape_10 = None
+        q_4 = qkv_5[0]
+        k_2 = qkv_5[1]
+        v_2 = qkv_5[2]
+        qkv_5 = None
+        q_5 = q_4 * 0.1767766952966369
+        q_4 = None
+        transpose_4 = k_2.transpose(-2, -1)
+        k_2 = None
+        attn_11 = q_5.matmul(transpose_4)
+        q_5 = transpose_4 = None
+        attn_12 = attn_11 + relative_position_bias_8
+        attn_11 = relative_position_bias_8 = None
+        attn_13 = torch.nn.functional.softmax(attn_12, dim=-1)
+        attn_12 = None
+        attn_14 = torch.nn.functional.dropout(attn_13, p=0.0, training=False)
+        attn_13 = None
+        matmul_5 = attn_14.matmul(v_2)
+        attn_14 = v_2 = None
+        transpose_5 = matmul_5.transpose(1, 2)
+        matmul_5 = None
+        x_31 = transpose_5.reshape(16, 49, 256)
+        transpose_5 = None
+        x_32 = torch._C._nn.linear(
+            x_31,
+            l_self_modules_features_modules_3_modules_0_modules_attn_modules_proj_parameters_weight_,
+            l_self_modules_features_modules_3_modules_0_modules_attn_modules_proj_parameters_bias_,
+        )
+        x_31 = l_self_modules_features_modules_3_modules_0_modules_attn_modules_proj_parameters_weight_ = l_self_modules_features_modules_3_modules_0_modules_attn_modules_proj_parameters_bias_ = (None)
+        x_33 = torch.nn.functional.dropout(x_32, p=0.0, training=False)
+        x_32 = None
+        x_34 = x_33.view(1, 4, 4, 7, 7, 256)
+        x_33 = None
+        permute_13 = x_34.permute(0, 1, 3, 2, 4, 5)
+        x_34 = None
+        x_35 = permute_13.reshape(1, 28, 28, 256)
+        permute_13 = None
+        getitem_18 = x_35[
+            (
+                slice(None, None, None),
+                slice(None, 28, None),
+                slice(None, 28, None),
+                slice(None, None, None),
+            )
+        ]
+        x_35 = None
+        x_36 = getitem_18.contiguous()
+        getitem_18 = None
+        _log_api_usage_once_4 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_4 = None
+        x_37 = x_27 + x_36
+        x_27 = x_36 = None
+        layer_norm_7 = torch.nn.functional.layer_norm(
+            x_37,
+            (256,),
+            l_self_modules_features_modules_3_modules_0_modules_norm2_parameters_weight_,
+            l_self_modules_features_modules_3_modules_0_modules_norm2_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_3_modules_0_modules_norm2_parameters_weight_ = (
+            l_self_modules_features_modules_3_modules_0_modules_norm2_parameters_bias_
+        ) = None
+        input_14 = torch._C._nn.linear(
+            layer_norm_7,
+            l_self_modules_features_modules_3_modules_0_modules_mlp_modules_0_parameters_weight_,
+            l_self_modules_features_modules_3_modules_0_modules_mlp_modules_0_parameters_bias_,
+        )
+        layer_norm_7 = l_self_modules_features_modules_3_modules_0_modules_mlp_modules_0_parameters_weight_ = l_self_modules_features_modules_3_modules_0_modules_mlp_modules_0_parameters_bias_ = (None)
+        input_15 = torch._C._nn.gelu(input_14, approximate="none")
+        input_14 = None
+        input_16 = torch.nn.functional.dropout(input_15, 0.0, False, False)
+        input_15 = None
+        input_17 = torch._C._nn.linear(
+            input_16,
+            l_self_modules_features_modules_3_modules_0_modules_mlp_modules_3_parameters_weight_,
+            l_self_modules_features_modules_3_modules_0_modules_mlp_modules_3_parameters_bias_,
+        )
+        input_16 = l_self_modules_features_modules_3_modules_0_modules_mlp_modules_3_parameters_weight_ = l_self_modules_features_modules_3_modules_0_modules_mlp_modules_3_parameters_bias_ = (None)
+        input_18 = torch.nn.functional.dropout(input_17, 0.0, False, False)
+        input_17 = None
+        _log_api_usage_once_5 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_5 = None
+        x_38 = x_37 + input_18
+        x_37 = input_18 = None
+        layer_norm_8 = torch.nn.functional.layer_norm(
+            x_38,
+            (256,),
+            l_self_modules_features_modules_3_modules_1_modules_norm1_parameters_weight_,
+            l_self_modules_features_modules_3_modules_1_modules_norm1_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_3_modules_1_modules_norm1_parameters_weight_ = (
+            l_self_modules_features_modules_3_modules_1_modules_norm1_parameters_bias_
+        ) = None
+        relative_position_bias_9 = l_self_modules_features_modules_3_modules_1_modules_attn_parameters_relative_position_bias_table_[
+            l_self_modules_features_modules_3_modules_1_modules_attn_buffers_relative_position_index_
+        ]
+        l_self_modules_features_modules_3_modules_1_modules_attn_parameters_relative_position_bias_table_ = l_self_modules_features_modules_3_modules_1_modules_attn_buffers_relative_position_index_ = (None)
+        relative_position_bias_10 = relative_position_bias_9.view(49, 49, -1)
+        relative_position_bias_9 = None
+        permute_14 = relative_position_bias_10.permute(2, 0, 1)
+        relative_position_bias_10 = None
+        contiguous_6 = permute_14.contiguous()
+        permute_14 = None
+        relative_position_bias_11 = contiguous_6.unsqueeze(0)
+        contiguous_6 = None
+        x_39 = torch._C._nn.pad(layer_norm_8, (0, 0, 0, 0, 0, 0), "constant", None)
+        layer_norm_8 = None
+        x_40 = torch.roll(x_39, shifts=(-3, -3), dims=(1, 2))
+        x_39 = None
+        x_41 = x_40.view(1, 4, 7, 4, 7, 256)
+        x_40 = None
+        permute_15 = x_41.permute(0, 1, 3, 2, 4, 5)
+        x_41 = None
+        x_42 = permute_15.reshape(16, 49, 256)
+        permute_15 = None
+        qkv_6 = torch._C._nn.linear(
+            x_42,
+            l_self_modules_features_modules_3_modules_1_modules_attn_modules_qkv_parameters_weight_,
+            l_self_modules_features_modules_3_modules_1_modules_attn_modules_qkv_parameters_bias_,
+        )
+        l_self_modules_features_modules_3_modules_1_modules_attn_modules_qkv_parameters_weight_ = l_self_modules_features_modules_3_modules_1_modules_attn_modules_qkv_parameters_bias_ = (None)
+        reshape_14 = qkv_6.reshape(16, 49, 3, 8, 32)
+        qkv_6 = None
+        qkv_7 = reshape_14.permute(2, 0, 3, 1, 4)
+        reshape_14 = None
+        q_6 = qkv_7[0]
+        k_3 = qkv_7[1]
+        v_3 = qkv_7[2]
+        qkv_7 = None
+        q_7 = q_6 * 0.1767766952966369
+        q_6 = None
+        transpose_6 = k_3.transpose(-2, -1)
+        k_3 = None
+        attn_15 = q_7.matmul(transpose_6)
+        q_7 = transpose_6 = None
+        attn_16 = attn_15 + relative_position_bias_11
+        attn_15 = relative_position_bias_11 = None
+        attn_mask_5 = x_42.new_zeros((28, 28))
+        x_42 = None
+        attn_mask_5[(slice(0, -7, None), slice(0, -7, None))] = 0
+        setitem_9 = attn_mask_5
+        setitem_9 = None
+        attn_mask_5[(slice(0, -7, None), slice(-7, -3, None))] = 1
+        setitem_10 = attn_mask_5
+        setitem_10 = None
+        attn_mask_5[(slice(0, -7, None), slice(-3, None, None))] = 2
+        setitem_11 = attn_mask_5
+        setitem_11 = None
+        attn_mask_5[(slice(-7, -3, None), slice(0, -7, None))] = 3
+        setitem_12 = attn_mask_5
+        setitem_12 = None
+        attn_mask_5[(slice(-7, -3, None), slice(-7, -3, None))] = 4
+        setitem_13 = attn_mask_5
+        setitem_13 = None
+        attn_mask_5[(slice(-7, -3, None), slice(-3, None, None))] = 5
+        setitem_14 = attn_mask_5
+        setitem_14 = None
+        attn_mask_5[(slice(-3, None, None), slice(0, -7, None))] = 6
+        setitem_15 = attn_mask_5
+        setitem_15 = None
+        attn_mask_5[(slice(-3, None, None), slice(-7, -3, None))] = 7
+        setitem_16 = attn_mask_5
+        setitem_16 = None
+        attn_mask_5[(slice(-3, None, None), slice(-3, None, None))] = 8
+        setitem_17 = attn_mask_5
+        setitem_17 = None
+        attn_mask_6 = attn_mask_5.view(4, 7, 4, 7)
+        attn_mask_5 = None
+        permute_17 = attn_mask_6.permute(0, 2, 1, 3)
+        attn_mask_6 = None
+        attn_mask_7 = permute_17.reshape(16, 49)
+        permute_17 = None
+        unsqueeze_8 = attn_mask_7.unsqueeze(1)
+        unsqueeze_9 = attn_mask_7.unsqueeze(2)
+        attn_mask_7 = None
+        attn_mask_8 = unsqueeze_8 - unsqueeze_9
+        unsqueeze_8 = unsqueeze_9 = None
+        ne_1 = attn_mask_8 != 0
+        masked_fill_2 = attn_mask_8.masked_fill(ne_1, -100.0)
+        ne_1 = None
+        eq_1 = attn_mask_8 == 0
+        attn_mask_8 = None
+        attn_mask_9 = masked_fill_2.masked_fill(eq_1, 0.0)
+        masked_fill_2 = eq_1 = None
+        attn_17 = attn_16.view(1, 16, 8, 49, 49)
+        attn_16 = None
+        unsqueeze_10 = attn_mask_9.unsqueeze(1)
+        attn_mask_9 = None
+        unsqueeze_11 = unsqueeze_10.unsqueeze(0)
+        unsqueeze_10 = None
+        attn_18 = attn_17 + unsqueeze_11
+        attn_17 = unsqueeze_11 = None
+        attn_19 = attn_18.view(-1, 8, 49, 49)
+        attn_18 = None
+        attn_20 = torch.nn.functional.softmax(attn_19, dim=-1)
+        attn_19 = None
+        attn_21 = torch.nn.functional.dropout(attn_20, p=0.0, training=False)
+        attn_20 = None
+        matmul_7 = attn_21.matmul(v_3)
+        attn_21 = v_3 = None
+        transpose_7 = matmul_7.transpose(1, 2)
+        matmul_7 = None
+        x_43 = transpose_7.reshape(16, 49, 256)
+        transpose_7 = None
+        x_44 = torch._C._nn.linear(
+            x_43,
+            l_self_modules_features_modules_3_modules_1_modules_attn_modules_proj_parameters_weight_,
+            l_self_modules_features_modules_3_modules_1_modules_attn_modules_proj_parameters_bias_,
+        )
+        x_43 = l_self_modules_features_modules_3_modules_1_modules_attn_modules_proj_parameters_weight_ = l_self_modules_features_modules_3_modules_1_modules_attn_modules_proj_parameters_bias_ = (None)
+        x_45 = torch.nn.functional.dropout(x_44, p=0.0, training=False)
+        x_44 = None
+        x_46 = x_45.view(1, 4, 4, 7, 7, 256)
+        x_45 = None
+        permute_18 = x_46.permute(0, 1, 3, 2, 4, 5)
+        x_46 = None
+        x_47 = permute_18.reshape(1, 28, 28, 256)
+        permute_18 = None
+        x_48 = torch.roll(x_47, shifts=(3, 3), dims=(1, 2))
+        x_47 = None
+        getitem_23 = x_48[
+            (
+                slice(None, None, None),
+                slice(None, 28, None),
+                slice(None, 28, None),
+                slice(None, None, None),
+            )
+        ]
+        x_48 = None
+        x_49 = getitem_23.contiguous()
+        getitem_23 = None
+        _log_api_usage_once_6 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_6 = None
+        x_50 = x_38 + x_49
+        x_38 = x_49 = None
+        layer_norm_9 = torch.nn.functional.layer_norm(
+            x_50,
+            (256,),
+            l_self_modules_features_modules_3_modules_1_modules_norm2_parameters_weight_,
+            l_self_modules_features_modules_3_modules_1_modules_norm2_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_3_modules_1_modules_norm2_parameters_weight_ = (
+            l_self_modules_features_modules_3_modules_1_modules_norm2_parameters_bias_
+        ) = None
+        input_19 = torch._C._nn.linear(
+            layer_norm_9,
+            l_self_modules_features_modules_3_modules_1_modules_mlp_modules_0_parameters_weight_,
+            l_self_modules_features_modules_3_modules_1_modules_mlp_modules_0_parameters_bias_,
+        )
+        layer_norm_9 = l_self_modules_features_modules_3_modules_1_modules_mlp_modules_0_parameters_weight_ = l_self_modules_features_modules_3_modules_1_modules_mlp_modules_0_parameters_bias_ = (None)
+        input_20 = torch._C._nn.gelu(input_19, approximate="none")
+        input_19 = None
+        input_21 = torch.nn.functional.dropout(input_20, 0.0, False, False)
+        input_20 = None
+        input_22 = torch._C._nn.linear(
+            input_21,
+            l_self_modules_features_modules_3_modules_1_modules_mlp_modules_3_parameters_weight_,
+            l_self_modules_features_modules_3_modules_1_modules_mlp_modules_3_parameters_bias_,
+        )
+        input_21 = l_self_modules_features_modules_3_modules_1_modules_mlp_modules_3_parameters_weight_ = l_self_modules_features_modules_3_modules_1_modules_mlp_modules_3_parameters_bias_ = (None)
+        input_23 = torch.nn.functional.dropout(input_22, 0.0, False, False)
+        input_22 = None
+        _log_api_usage_once_7 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_7 = None
+        x_51 = x_50 + input_23
+        x_50 = input_23 = None
+        x_52 = torch._C._nn.pad(x_51, (0, 0, 0, 0, 0, 0), "constant", None)
+        x_51 = None
+        x0_1 = x_52[
+            (Ellipsis, slice(0, None, 2), slice(0, None, 2), slice(None, None, None))
+        ]
+        x1_1 = x_52[
+            (Ellipsis, slice(1, None, 2), slice(0, None, 2), slice(None, None, None))
+        ]
+        x2_1 = x_52[
+            (Ellipsis, slice(0, None, 2), slice(1, None, 2), slice(None, None, None))
+        ]
+        x3_1 = x_52[
+            (Ellipsis, slice(1, None, 2), slice(1, None, 2), slice(None, None, None))
+        ]
+        x_52 = None
+        x_53 = torch.cat([x0_1, x1_1, x2_1, x3_1], -1)
+        x0_1 = x1_1 = x2_1 = x3_1 = None
+        x_54 = torch.nn.functional.layer_norm(
+            x_53,
+            (1024,),
+            l_self_modules_features_modules_4_modules_norm_parameters_weight_,
+            l_self_modules_features_modules_4_modules_norm_parameters_bias_,
+            1e-05,
+        )
+        x_53 = (
+            l_self_modules_features_modules_4_modules_norm_parameters_weight_
+        ) = l_self_modules_features_modules_4_modules_norm_parameters_bias_ = None
+        x_55 = torch._C._nn.linear(
+            x_54,
+            l_self_modules_features_modules_4_modules_reduction_parameters_weight_,
+            None,
+        )
+        x_54 = (
+            l_self_modules_features_modules_4_modules_reduction_parameters_weight_
+        ) = None
+        layer_norm_11 = torch.nn.functional.layer_norm(
+            x_55,
+            (512,),
+            l_self_modules_features_modules_5_modules_0_modules_norm1_parameters_weight_,
+            l_self_modules_features_modules_5_modules_0_modules_norm1_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_5_modules_0_modules_norm1_parameters_weight_ = (
+            l_self_modules_features_modules_5_modules_0_modules_norm1_parameters_bias_
+        ) = None
+        relative_position_bias_12 = l_self_modules_features_modules_5_modules_0_modules_attn_parameters_relative_position_bias_table_[
+            l_self_modules_features_modules_5_modules_0_modules_attn_buffers_relative_position_index_
+        ]
+        l_self_modules_features_modules_5_modules_0_modules_attn_parameters_relative_position_bias_table_ = l_self_modules_features_modules_5_modules_0_modules_attn_buffers_relative_position_index_ = (None)
+        relative_position_bias_13 = relative_position_bias_12.view(49, 49, -1)
+        relative_position_bias_12 = None
+        permute_19 = relative_position_bias_13.permute(2, 0, 1)
+        relative_position_bias_13 = None
+        contiguous_8 = permute_19.contiguous()
+        permute_19 = None
+        relative_position_bias_14 = contiguous_8.unsqueeze(0)
+        contiguous_8 = None
+        x_56 = torch._C._nn.pad(layer_norm_11, (0, 0, 0, 0, 0, 0), "constant", None)
+        layer_norm_11 = None
+        x_57 = x_56.view(1, 2, 7, 2, 7, 512)
+        x_56 = None
+        permute_20 = x_57.permute(0, 1, 3, 2, 4, 5)
+        x_57 = None
+        x_58 = permute_20.reshape(4, 49, 512)
+        permute_20 = None
+        qkv_8 = torch._C._nn.linear(
+            x_58,
+            l_self_modules_features_modules_5_modules_0_modules_attn_modules_qkv_parameters_weight_,
+            l_self_modules_features_modules_5_modules_0_modules_attn_modules_qkv_parameters_bias_,
+        )
+        x_58 = l_self_modules_features_modules_5_modules_0_modules_attn_modules_qkv_parameters_weight_ = l_self_modules_features_modules_5_modules_0_modules_attn_modules_qkv_parameters_bias_ = (None)
+        reshape_19 = qkv_8.reshape(4, 49, 3, 16, 32)
+        qkv_8 = None
+        qkv_9 = reshape_19.permute(2, 0, 3, 1, 4)
+        reshape_19 = None
+        q_8 = qkv_9[0]
+        k_4 = qkv_9[1]
+        v_4 = qkv_9[2]
+        qkv_9 = None
+        q_9 = q_8 * 0.1767766952966369
+        q_8 = None
+        transpose_8 = k_4.transpose(-2, -1)
+        k_4 = None
+        attn_22 = q_9.matmul(transpose_8)
+        q_9 = transpose_8 = None
+        attn_23 = attn_22 + relative_position_bias_14
+        attn_22 = relative_position_bias_14 = None
+        attn_24 = torch.nn.functional.softmax(attn_23, dim=-1)
+        attn_23 = None
+        attn_25 = torch.nn.functional.dropout(attn_24, p=0.0, training=False)
+        attn_24 = None
+        matmul_9 = attn_25.matmul(v_4)
+        attn_25 = v_4 = None
+        transpose_9 = matmul_9.transpose(1, 2)
+        matmul_9 = None
+        x_59 = transpose_9.reshape(4, 49, 512)
+        transpose_9 = None
+        x_60 = torch._C._nn.linear(
+            x_59,
+            l_self_modules_features_modules_5_modules_0_modules_attn_modules_proj_parameters_weight_,
+            l_self_modules_features_modules_5_modules_0_modules_attn_modules_proj_parameters_bias_,
+        )
+        x_59 = l_self_modules_features_modules_5_modules_0_modules_attn_modules_proj_parameters_weight_ = l_self_modules_features_modules_5_modules_0_modules_attn_modules_proj_parameters_bias_ = (None)
+        x_61 = torch.nn.functional.dropout(x_60, p=0.0, training=False)
+        x_60 = None
+        x_62 = x_61.view(1, 2, 2, 7, 7, 512)
+        x_61 = None
+        permute_22 = x_62.permute(0, 1, 3, 2, 4, 5)
+        x_62 = None
+        x_63 = permute_22.reshape(1, 14, 14, 512)
+        permute_22 = None
+        getitem_32 = x_63[
+            (
+                slice(None, None, None),
+                slice(None, 14, None),
+                slice(None, 14, None),
+                slice(None, None, None),
+            )
+        ]
+        x_63 = None
+        x_64 = getitem_32.contiguous()
+        getitem_32 = None
+        _log_api_usage_once_8 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_8 = None
+        x_65 = x_55 + x_64
+        x_55 = x_64 = None
+        layer_norm_12 = torch.nn.functional.layer_norm(
+            x_65,
+            (512,),
+            l_self_modules_features_modules_5_modules_0_modules_norm2_parameters_weight_,
+            l_self_modules_features_modules_5_modules_0_modules_norm2_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_5_modules_0_modules_norm2_parameters_weight_ = (
+            l_self_modules_features_modules_5_modules_0_modules_norm2_parameters_bias_
+        ) = None
+        input_24 = torch._C._nn.linear(
+            layer_norm_12,
+            l_self_modules_features_modules_5_modules_0_modules_mlp_modules_0_parameters_weight_,
+            l_self_modules_features_modules_5_modules_0_modules_mlp_modules_0_parameters_bias_,
+        )
+        layer_norm_12 = l_self_modules_features_modules_5_modules_0_modules_mlp_modules_0_parameters_weight_ = l_self_modules_features_modules_5_modules_0_modules_mlp_modules_0_parameters_bias_ = (None)
+        input_25 = torch._C._nn.gelu(input_24, approximate="none")
+        input_24 = None
+        input_26 = torch.nn.functional.dropout(input_25, 0.0, False, False)
+        input_25 = None
+        input_27 = torch._C._nn.linear(
+            input_26,
+            l_self_modules_features_modules_5_modules_0_modules_mlp_modules_3_parameters_weight_,
+            l_self_modules_features_modules_5_modules_0_modules_mlp_modules_3_parameters_bias_,
+        )
+        input_26 = l_self_modules_features_modules_5_modules_0_modules_mlp_modules_3_parameters_weight_ = l_self_modules_features_modules_5_modules_0_modules_mlp_modules_3_parameters_bias_ = (None)
+        input_28 = torch.nn.functional.dropout(input_27, 0.0, False, False)
+        input_27 = None
+        _log_api_usage_once_9 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_9 = None
+        x_66 = x_65 + input_28
+        x_65 = input_28 = None
+        layer_norm_13 = torch.nn.functional.layer_norm(
+            x_66,
+            (512,),
+            l_self_modules_features_modules_5_modules_1_modules_norm1_parameters_weight_,
+            l_self_modules_features_modules_5_modules_1_modules_norm1_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_5_modules_1_modules_norm1_parameters_weight_ = (
+            l_self_modules_features_modules_5_modules_1_modules_norm1_parameters_bias_
+        ) = None
+        relative_position_bias_15 = l_self_modules_features_modules_5_modules_1_modules_attn_parameters_relative_position_bias_table_[
+            l_self_modules_features_modules_5_modules_1_modules_attn_buffers_relative_position_index_
+        ]
+        l_self_modules_features_modules_5_modules_1_modules_attn_parameters_relative_position_bias_table_ = l_self_modules_features_modules_5_modules_1_modules_attn_buffers_relative_position_index_ = (None)
+        relative_position_bias_16 = relative_position_bias_15.view(49, 49, -1)
+        relative_position_bias_15 = None
+        permute_23 = relative_position_bias_16.permute(2, 0, 1)
+        relative_position_bias_16 = None
+        contiguous_10 = permute_23.contiguous()
+        permute_23 = None
+        relative_position_bias_17 = contiguous_10.unsqueeze(0)
+        contiguous_10 = None
+        x_67 = torch._C._nn.pad(layer_norm_13, (0, 0, 0, 0, 0, 0), "constant", None)
+        layer_norm_13 = None
+        x_68 = torch.roll(x_67, shifts=(-3, -3), dims=(1, 2))
+        x_67 = None
+        x_69 = x_68.view(1, 2, 7, 2, 7, 512)
+        x_68 = None
+        permute_24 = x_69.permute(0, 1, 3, 2, 4, 5)
+        x_69 = None
+        x_70 = permute_24.reshape(4, 49, 512)
+        permute_24 = None
+        qkv_10 = torch._C._nn.linear(
+            x_70,
+            l_self_modules_features_modules_5_modules_1_modules_attn_modules_qkv_parameters_weight_,
+            l_self_modules_features_modules_5_modules_1_modules_attn_modules_qkv_parameters_bias_,
+        )
+        l_self_modules_features_modules_5_modules_1_modules_attn_modules_qkv_parameters_weight_ = l_self_modules_features_modules_5_modules_1_modules_attn_modules_qkv_parameters_bias_ = (None)
+        reshape_23 = qkv_10.reshape(4, 49, 3, 16, 32)
+        qkv_10 = None
+        qkv_11 = reshape_23.permute(2, 0, 3, 1, 4)
+        reshape_23 = None
+        q_10 = qkv_11[0]
+        k_5 = qkv_11[1]
+        v_5 = qkv_11[2]
+        qkv_11 = None
+        q_11 = q_10 * 0.1767766952966369
+        q_10 = None
+        transpose_10 = k_5.transpose(-2, -1)
+        k_5 = None
+        attn_26 = q_11.matmul(transpose_10)
+        q_11 = transpose_10 = None
+        attn_27 = attn_26 + relative_position_bias_17
+        attn_26 = relative_position_bias_17 = None
+        attn_mask_10 = x_70.new_zeros((14, 14))
+        x_70 = None
+        attn_mask_10[(slice(0, -7, None), slice(0, -7, None))] = 0
+        setitem_18 = attn_mask_10
+        setitem_18 = None
+        attn_mask_10[(slice(0, -7, None), slice(-7, -3, None))] = 1
+        setitem_19 = attn_mask_10
+        setitem_19 = None
+        attn_mask_10[(slice(0, -7, None), slice(-3, None, None))] = 2
+        setitem_20 = attn_mask_10
+        setitem_20 = None
+        attn_mask_10[(slice(-7, -3, None), slice(0, -7, None))] = 3
+        setitem_21 = attn_mask_10
+        setitem_21 = None
+        attn_mask_10[(slice(-7, -3, None), slice(-7, -3, None))] = 4
+        setitem_22 = attn_mask_10
+        setitem_22 = None
+        attn_mask_10[(slice(-7, -3, None), slice(-3, None, None))] = 5
+        setitem_23 = attn_mask_10
+        setitem_23 = None
+        attn_mask_10[(slice(-3, None, None), slice(0, -7, None))] = 6
+        setitem_24 = attn_mask_10
+        setitem_24 = None
+        attn_mask_10[(slice(-3, None, None), slice(-7, -3, None))] = 7
+        setitem_25 = attn_mask_10
+        setitem_25 = None
+        attn_mask_10[(slice(-3, None, None), slice(-3, None, None))] = 8
+        setitem_26 = attn_mask_10
+        setitem_26 = None
+        attn_mask_11 = attn_mask_10.view(2, 7, 2, 7)
+        attn_mask_10 = None
+        permute_26 = attn_mask_11.permute(0, 2, 1, 3)
+        attn_mask_11 = None
+        attn_mask_12 = permute_26.reshape(4, 49)
+        permute_26 = None
+        unsqueeze_14 = attn_mask_12.unsqueeze(1)
+        unsqueeze_15 = attn_mask_12.unsqueeze(2)
+        attn_mask_12 = None
+        attn_mask_13 = unsqueeze_14 - unsqueeze_15
+        unsqueeze_14 = unsqueeze_15 = None
+        ne_2 = attn_mask_13 != 0
+        masked_fill_4 = attn_mask_13.masked_fill(ne_2, -100.0)
+        ne_2 = None
+        eq_2 = attn_mask_13 == 0
+        attn_mask_13 = None
+        attn_mask_14 = masked_fill_4.masked_fill(eq_2, 0.0)
+        masked_fill_4 = eq_2 = None
+        attn_28 = attn_27.view(1, 4, 16, 49, 49)
+        attn_27 = None
+        unsqueeze_16 = attn_mask_14.unsqueeze(1)
+        attn_mask_14 = None
+        unsqueeze_17 = unsqueeze_16.unsqueeze(0)
+        unsqueeze_16 = None
+        attn_29 = attn_28 + unsqueeze_17
+        attn_28 = unsqueeze_17 = None
+        attn_30 = attn_29.view(-1, 16, 49, 49)
+        attn_29 = None
+        attn_31 = torch.nn.functional.softmax(attn_30, dim=-1)
+        attn_30 = None
+        attn_32 = torch.nn.functional.dropout(attn_31, p=0.0, training=False)
+        attn_31 = None
+        matmul_11 = attn_32.matmul(v_5)
+        attn_32 = v_5 = None
+        transpose_11 = matmul_11.transpose(1, 2)
+        matmul_11 = None
+        x_71 = transpose_11.reshape(4, 49, 512)
+        transpose_11 = None
+        x_72 = torch._C._nn.linear(
+            x_71,
+            l_self_modules_features_modules_5_modules_1_modules_attn_modules_proj_parameters_weight_,
+            l_self_modules_features_modules_5_modules_1_modules_attn_modules_proj_parameters_bias_,
+        )
+        x_71 = l_self_modules_features_modules_5_modules_1_modules_attn_modules_proj_parameters_weight_ = l_self_modules_features_modules_5_modules_1_modules_attn_modules_proj_parameters_bias_ = (None)
+        x_73 = torch.nn.functional.dropout(x_72, p=0.0, training=False)
+        x_72 = None
+        x_74 = x_73.view(1, 2, 2, 7, 7, 512)
+        x_73 = None
+        permute_27 = x_74.permute(0, 1, 3, 2, 4, 5)
+        x_74 = None
+        x_75 = permute_27.reshape(1, 14, 14, 512)
+        permute_27 = None
+        x_76 = torch.roll(x_75, shifts=(3, 3), dims=(1, 2))
+        x_75 = None
+        getitem_37 = x_76[
+            (
+                slice(None, None, None),
+                slice(None, 14, None),
+                slice(None, 14, None),
+                slice(None, None, None),
+            )
+        ]
+        x_76 = None
+        x_77 = getitem_37.contiguous()
+        getitem_37 = None
+        _log_api_usage_once_10 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_10 = None
+        x_78 = x_66 + x_77
+        x_66 = x_77 = None
+        layer_norm_14 = torch.nn.functional.layer_norm(
+            x_78,
+            (512,),
+            l_self_modules_features_modules_5_modules_1_modules_norm2_parameters_weight_,
+            l_self_modules_features_modules_5_modules_1_modules_norm2_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_5_modules_1_modules_norm2_parameters_weight_ = (
+            l_self_modules_features_modules_5_modules_1_modules_norm2_parameters_bias_
+        ) = None
+        input_29 = torch._C._nn.linear(
+            layer_norm_14,
+            l_self_modules_features_modules_5_modules_1_modules_mlp_modules_0_parameters_weight_,
+            l_self_modules_features_modules_5_modules_1_modules_mlp_modules_0_parameters_bias_,
+        )
+        layer_norm_14 = l_self_modules_features_modules_5_modules_1_modules_mlp_modules_0_parameters_weight_ = l_self_modules_features_modules_5_modules_1_modules_mlp_modules_0_parameters_bias_ = (None)
+        input_30 = torch._C._nn.gelu(input_29, approximate="none")
+        input_29 = None
+        input_31 = torch.nn.functional.dropout(input_30, 0.0, False, False)
+        input_30 = None
+        input_32 = torch._C._nn.linear(
+            input_31,
+            l_self_modules_features_modules_5_modules_1_modules_mlp_modules_3_parameters_weight_,
+            l_self_modules_features_modules_5_modules_1_modules_mlp_modules_3_parameters_bias_,
+        )
+        input_31 = l_self_modules_features_modules_5_modules_1_modules_mlp_modules_3_parameters_weight_ = l_self_modules_features_modules_5_modules_1_modules_mlp_modules_3_parameters_bias_ = (None)
+        input_33 = torch.nn.functional.dropout(input_32, 0.0, False, False)
+        input_32 = None
+        _log_api_usage_once_11 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_11 = None
+        x_79 = x_78 + input_33
+        x_78 = input_33 = None
+        layer_norm_15 = torch.nn.functional.layer_norm(
+            x_79,
+            (512,),
+            l_self_modules_features_modules_5_modules_2_modules_norm1_parameters_weight_,
+            l_self_modules_features_modules_5_modules_2_modules_norm1_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_5_modules_2_modules_norm1_parameters_weight_ = (
+            l_self_modules_features_modules_5_modules_2_modules_norm1_parameters_bias_
+        ) = None
+        relative_position_bias_18 = l_self_modules_features_modules_5_modules_2_modules_attn_parameters_relative_position_bias_table_[
+            l_self_modules_features_modules_5_modules_2_modules_attn_buffers_relative_position_index_
+        ]
+        l_self_modules_features_modules_5_modules_2_modules_attn_parameters_relative_position_bias_table_ = l_self_modules_features_modules_5_modules_2_modules_attn_buffers_relative_position_index_ = (None)
+        relative_position_bias_19 = relative_position_bias_18.view(49, 49, -1)
+        relative_position_bias_18 = None
+        permute_28 = relative_position_bias_19.permute(2, 0, 1)
+        relative_position_bias_19 = None
+        contiguous_12 = permute_28.contiguous()
+        permute_28 = None
+        relative_position_bias_20 = contiguous_12.unsqueeze(0)
+        contiguous_12 = None
+        x_80 = torch._C._nn.pad(layer_norm_15, (0, 0, 0, 0, 0, 0), "constant", None)
+        layer_norm_15 = None
+        x_81 = x_80.view(1, 2, 7, 2, 7, 512)
+        x_80 = None
+        permute_29 = x_81.permute(0, 1, 3, 2, 4, 5)
+        x_81 = None
+        x_82 = permute_29.reshape(4, 49, 512)
+        permute_29 = None
+        qkv_12 = torch._C._nn.linear(
+            x_82,
+            l_self_modules_features_modules_5_modules_2_modules_attn_modules_qkv_parameters_weight_,
+            l_self_modules_features_modules_5_modules_2_modules_attn_modules_qkv_parameters_bias_,
+        )
+        x_82 = l_self_modules_features_modules_5_modules_2_modules_attn_modules_qkv_parameters_weight_ = l_self_modules_features_modules_5_modules_2_modules_attn_modules_qkv_parameters_bias_ = (None)
+        reshape_28 = qkv_12.reshape(4, 49, 3, 16, 32)
+        qkv_12 = None
+        qkv_13 = reshape_28.permute(2, 0, 3, 1, 4)
+        reshape_28 = None
+        q_12 = qkv_13[0]
+        k_6 = qkv_13[1]
+        v_6 = qkv_13[2]
+        qkv_13 = None
+        q_13 = q_12 * 0.1767766952966369
+        q_12 = None
+        transpose_12 = k_6.transpose(-2, -1)
+        k_6 = None
+        attn_33 = q_13.matmul(transpose_12)
+        q_13 = transpose_12 = None
+        attn_34 = attn_33 + relative_position_bias_20
+        attn_33 = relative_position_bias_20 = None
+        attn_35 = torch.nn.functional.softmax(attn_34, dim=-1)
+        attn_34 = None
+        attn_36 = torch.nn.functional.dropout(attn_35, p=0.0, training=False)
+        attn_35 = None
+        matmul_13 = attn_36.matmul(v_6)
+        attn_36 = v_6 = None
+        transpose_13 = matmul_13.transpose(1, 2)
+        matmul_13 = None
+        x_83 = transpose_13.reshape(4, 49, 512)
+        transpose_13 = None
+        x_84 = torch._C._nn.linear(
+            x_83,
+            l_self_modules_features_modules_5_modules_2_modules_attn_modules_proj_parameters_weight_,
+            l_self_modules_features_modules_5_modules_2_modules_attn_modules_proj_parameters_bias_,
+        )
+        x_83 = l_self_modules_features_modules_5_modules_2_modules_attn_modules_proj_parameters_weight_ = l_self_modules_features_modules_5_modules_2_modules_attn_modules_proj_parameters_bias_ = (None)
+        x_85 = torch.nn.functional.dropout(x_84, p=0.0, training=False)
+        x_84 = None
+        x_86 = x_85.view(1, 2, 2, 7, 7, 512)
+        x_85 = None
+        permute_31 = x_86.permute(0, 1, 3, 2, 4, 5)
+        x_86 = None
+        x_87 = permute_31.reshape(1, 14, 14, 512)
+        permute_31 = None
+        getitem_42 = x_87[
+            (
+                slice(None, None, None),
+                slice(None, 14, None),
+                slice(None, 14, None),
+                slice(None, None, None),
+            )
+        ]
+        x_87 = None
+        x_88 = getitem_42.contiguous()
+        getitem_42 = None
+        _log_api_usage_once_12 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_12 = None
+        x_89 = x_79 + x_88
+        x_79 = x_88 = None
+        layer_norm_16 = torch.nn.functional.layer_norm(
+            x_89,
+            (512,),
+            l_self_modules_features_modules_5_modules_2_modules_norm2_parameters_weight_,
+            l_self_modules_features_modules_5_modules_2_modules_norm2_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_5_modules_2_modules_norm2_parameters_weight_ = (
+            l_self_modules_features_modules_5_modules_2_modules_norm2_parameters_bias_
+        ) = None
+        input_34 = torch._C._nn.linear(
+            layer_norm_16,
+            l_self_modules_features_modules_5_modules_2_modules_mlp_modules_0_parameters_weight_,
+            l_self_modules_features_modules_5_modules_2_modules_mlp_modules_0_parameters_bias_,
+        )
+        layer_norm_16 = l_self_modules_features_modules_5_modules_2_modules_mlp_modules_0_parameters_weight_ = l_self_modules_features_modules_5_modules_2_modules_mlp_modules_0_parameters_bias_ = (None)
+        input_35 = torch._C._nn.gelu(input_34, approximate="none")
+        input_34 = None
+        input_36 = torch.nn.functional.dropout(input_35, 0.0, False, False)
+        input_35 = None
+        input_37 = torch._C._nn.linear(
+            input_36,
+            l_self_modules_features_modules_5_modules_2_modules_mlp_modules_3_parameters_weight_,
+            l_self_modules_features_modules_5_modules_2_modules_mlp_modules_3_parameters_bias_,
+        )
+        input_36 = l_self_modules_features_modules_5_modules_2_modules_mlp_modules_3_parameters_weight_ = l_self_modules_features_modules_5_modules_2_modules_mlp_modules_3_parameters_bias_ = (None)
+        input_38 = torch.nn.functional.dropout(input_37, 0.0, False, False)
+        input_37 = None
+        _log_api_usage_once_13 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_13 = None
+        x_90 = x_89 + input_38
+        x_89 = input_38 = None
+        layer_norm_17 = torch.nn.functional.layer_norm(
+            x_90,
+            (512,),
+            l_self_modules_features_modules_5_modules_3_modules_norm1_parameters_weight_,
+            l_self_modules_features_modules_5_modules_3_modules_norm1_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_5_modules_3_modules_norm1_parameters_weight_ = (
+            l_self_modules_features_modules_5_modules_3_modules_norm1_parameters_bias_
+        ) = None
+        relative_position_bias_21 = l_self_modules_features_modules_5_modules_3_modules_attn_parameters_relative_position_bias_table_[
+            l_self_modules_features_modules_5_modules_3_modules_attn_buffers_relative_position_index_
+        ]
+        l_self_modules_features_modules_5_modules_3_modules_attn_parameters_relative_position_bias_table_ = l_self_modules_features_modules_5_modules_3_modules_attn_buffers_relative_position_index_ = (None)
+        relative_position_bias_22 = relative_position_bias_21.view(49, 49, -1)
+        relative_position_bias_21 = None
+        permute_32 = relative_position_bias_22.permute(2, 0, 1)
+        relative_position_bias_22 = None
+        contiguous_14 = permute_32.contiguous()
+        permute_32 = None
+        relative_position_bias_23 = contiguous_14.unsqueeze(0)
+        contiguous_14 = None
+        x_91 = torch._C._nn.pad(layer_norm_17, (0, 0, 0, 0, 0, 0), "constant", None)
+        layer_norm_17 = None
+        x_92 = torch.roll(x_91, shifts=(-3, -3), dims=(1, 2))
+        x_91 = None
+        x_93 = x_92.view(1, 2, 7, 2, 7, 512)
+        x_92 = None
+        permute_33 = x_93.permute(0, 1, 3, 2, 4, 5)
+        x_93 = None
+        x_94 = permute_33.reshape(4, 49, 512)
+        permute_33 = None
+        qkv_14 = torch._C._nn.linear(
+            x_94,
+            l_self_modules_features_modules_5_modules_3_modules_attn_modules_qkv_parameters_weight_,
+            l_self_modules_features_modules_5_modules_3_modules_attn_modules_qkv_parameters_bias_,
+        )
+        l_self_modules_features_modules_5_modules_3_modules_attn_modules_qkv_parameters_weight_ = l_self_modules_features_modules_5_modules_3_modules_attn_modules_qkv_parameters_bias_ = (None)
+        reshape_32 = qkv_14.reshape(4, 49, 3, 16, 32)
+        qkv_14 = None
+        qkv_15 = reshape_32.permute(2, 0, 3, 1, 4)
+        reshape_32 = None
+        q_14 = qkv_15[0]
+        k_7 = qkv_15[1]
+        v_7 = qkv_15[2]
+        qkv_15 = None
+        q_15 = q_14 * 0.1767766952966369
+        q_14 = None
+        transpose_14 = k_7.transpose(-2, -1)
+        k_7 = None
+        attn_37 = q_15.matmul(transpose_14)
+        q_15 = transpose_14 = None
+        attn_38 = attn_37 + relative_position_bias_23
+        attn_37 = relative_position_bias_23 = None
+        attn_mask_15 = x_94.new_zeros((14, 14))
+        x_94 = None
+        attn_mask_15[(slice(0, -7, None), slice(0, -7, None))] = 0
+        setitem_27 = attn_mask_15
+        setitem_27 = None
+        attn_mask_15[(slice(0, -7, None), slice(-7, -3, None))] = 1
+        setitem_28 = attn_mask_15
+        setitem_28 = None
+        attn_mask_15[(slice(0, -7, None), slice(-3, None, None))] = 2
+        setitem_29 = attn_mask_15
+        setitem_29 = None
+        attn_mask_15[(slice(-7, -3, None), slice(0, -7, None))] = 3
+        setitem_30 = attn_mask_15
+        setitem_30 = None
+        attn_mask_15[(slice(-7, -3, None), slice(-7, -3, None))] = 4
+        setitem_31 = attn_mask_15
+        setitem_31 = None
+        attn_mask_15[(slice(-7, -3, None), slice(-3, None, None))] = 5
+        setitem_32 = attn_mask_15
+        setitem_32 = None
+        attn_mask_15[(slice(-3, None, None), slice(0, -7, None))] = 6
+        setitem_33 = attn_mask_15
+        setitem_33 = None
+        attn_mask_15[(slice(-3, None, None), slice(-7, -3, None))] = 7
+        setitem_34 = attn_mask_15
+        setitem_34 = None
+        attn_mask_15[(slice(-3, None, None), slice(-3, None, None))] = 8
+        setitem_35 = attn_mask_15
+        setitem_35 = None
+        attn_mask_16 = attn_mask_15.view(2, 7, 2, 7)
+        attn_mask_15 = None
+        permute_35 = attn_mask_16.permute(0, 2, 1, 3)
+        attn_mask_16 = None
+        attn_mask_17 = permute_35.reshape(4, 49)
+        permute_35 = None
+        unsqueeze_20 = attn_mask_17.unsqueeze(1)
+        unsqueeze_21 = attn_mask_17.unsqueeze(2)
+        attn_mask_17 = None
+        attn_mask_18 = unsqueeze_20 - unsqueeze_21
+        unsqueeze_20 = unsqueeze_21 = None
+        ne_3 = attn_mask_18 != 0
+        masked_fill_6 = attn_mask_18.masked_fill(ne_3, -100.0)
+        ne_3 = None
+        eq_3 = attn_mask_18 == 0
+        attn_mask_18 = None
+        attn_mask_19 = masked_fill_6.masked_fill(eq_3, 0.0)
+        masked_fill_6 = eq_3 = None
+        attn_39 = attn_38.view(1, 4, 16, 49, 49)
+        attn_38 = None
+        unsqueeze_22 = attn_mask_19.unsqueeze(1)
+        attn_mask_19 = None
+        unsqueeze_23 = unsqueeze_22.unsqueeze(0)
+        unsqueeze_22 = None
+        attn_40 = attn_39 + unsqueeze_23
+        attn_39 = unsqueeze_23 = None
+        attn_41 = attn_40.view(-1, 16, 49, 49)
+        attn_40 = None
+        attn_42 = torch.nn.functional.softmax(attn_41, dim=-1)
+        attn_41 = None
+        attn_43 = torch.nn.functional.dropout(attn_42, p=0.0, training=False)
+        attn_42 = None
+        matmul_15 = attn_43.matmul(v_7)
+        attn_43 = v_7 = None
+        transpose_15 = matmul_15.transpose(1, 2)
+        matmul_15 = None
+        x_95 = transpose_15.reshape(4, 49, 512)
+        transpose_15 = None
+        x_96 = torch._C._nn.linear(
+            x_95,
+            l_self_modules_features_modules_5_modules_3_modules_attn_modules_proj_parameters_weight_,
+            l_self_modules_features_modules_5_modules_3_modules_attn_modules_proj_parameters_bias_,
+        )
+        x_95 = l_self_modules_features_modules_5_modules_3_modules_attn_modules_proj_parameters_weight_ = l_self_modules_features_modules_5_modules_3_modules_attn_modules_proj_parameters_bias_ = (None)
+        x_97 = torch.nn.functional.dropout(x_96, p=0.0, training=False)
+        x_96 = None
+        x_98 = x_97.view(1, 2, 2, 7, 7, 512)
+        x_97 = None
+        permute_36 = x_98.permute(0, 1, 3, 2, 4, 5)
+        x_98 = None
+        x_99 = permute_36.reshape(1, 14, 14, 512)
+        permute_36 = None
+        x_100 = torch.roll(x_99, shifts=(3, 3), dims=(1, 2))
+        x_99 = None
+        getitem_47 = x_100[
+            (
+                slice(None, None, None),
+                slice(None, 14, None),
+                slice(None, 14, None),
+                slice(None, None, None),
+            )
+        ]
+        x_100 = None
+        x_101 = getitem_47.contiguous()
+        getitem_47 = None
+        _log_api_usage_once_14 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_14 = None
+        x_102 = x_90 + x_101
+        x_90 = x_101 = None
+        layer_norm_18 = torch.nn.functional.layer_norm(
+            x_102,
+            (512,),
+            l_self_modules_features_modules_5_modules_3_modules_norm2_parameters_weight_,
+            l_self_modules_features_modules_5_modules_3_modules_norm2_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_5_modules_3_modules_norm2_parameters_weight_ = (
+            l_self_modules_features_modules_5_modules_3_modules_norm2_parameters_bias_
+        ) = None
+        input_39 = torch._C._nn.linear(
+            layer_norm_18,
+            l_self_modules_features_modules_5_modules_3_modules_mlp_modules_0_parameters_weight_,
+            l_self_modules_features_modules_5_modules_3_modules_mlp_modules_0_parameters_bias_,
+        )
+        layer_norm_18 = l_self_modules_features_modules_5_modules_3_modules_mlp_modules_0_parameters_weight_ = l_self_modules_features_modules_5_modules_3_modules_mlp_modules_0_parameters_bias_ = (None)
+        input_40 = torch._C._nn.gelu(input_39, approximate="none")
+        input_39 = None
+        input_41 = torch.nn.functional.dropout(input_40, 0.0, False, False)
+        input_40 = None
+        input_42 = torch._C._nn.linear(
+            input_41,
+            l_self_modules_features_modules_5_modules_3_modules_mlp_modules_3_parameters_weight_,
+            l_self_modules_features_modules_5_modules_3_modules_mlp_modules_3_parameters_bias_,
+        )
+        input_41 = l_self_modules_features_modules_5_modules_3_modules_mlp_modules_3_parameters_weight_ = l_self_modules_features_modules_5_modules_3_modules_mlp_modules_3_parameters_bias_ = (None)
+        input_43 = torch.nn.functional.dropout(input_42, 0.0, False, False)
+        input_42 = None
+        _log_api_usage_once_15 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_15 = None
+        x_103 = x_102 + input_43
+        x_102 = input_43 = None
+        layer_norm_19 = torch.nn.functional.layer_norm(
+            x_103,
+            (512,),
+            l_self_modules_features_modules_5_modules_4_modules_norm1_parameters_weight_,
+            l_self_modules_features_modules_5_modules_4_modules_norm1_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_5_modules_4_modules_norm1_parameters_weight_ = (
+            l_self_modules_features_modules_5_modules_4_modules_norm1_parameters_bias_
+        ) = None
+        relative_position_bias_24 = l_self_modules_features_modules_5_modules_4_modules_attn_parameters_relative_position_bias_table_[
+            l_self_modules_features_modules_5_modules_4_modules_attn_buffers_relative_position_index_
+        ]
+        l_self_modules_features_modules_5_modules_4_modules_attn_parameters_relative_position_bias_table_ = l_self_modules_features_modules_5_modules_4_modules_attn_buffers_relative_position_index_ = (None)
+        relative_position_bias_25 = relative_position_bias_24.view(49, 49, -1)
+        relative_position_bias_24 = None
+        permute_37 = relative_position_bias_25.permute(2, 0, 1)
+        relative_position_bias_25 = None
+        contiguous_16 = permute_37.contiguous()
+        permute_37 = None
+        relative_position_bias_26 = contiguous_16.unsqueeze(0)
+        contiguous_16 = None
+        x_104 = torch._C._nn.pad(layer_norm_19, (0, 0, 0, 0, 0, 0), "constant", None)
+        layer_norm_19 = None
+        x_105 = x_104.view(1, 2, 7, 2, 7, 512)
+        x_104 = None
+        permute_38 = x_105.permute(0, 1, 3, 2, 4, 5)
+        x_105 = None
+        x_106 = permute_38.reshape(4, 49, 512)
+        permute_38 = None
+        qkv_16 = torch._C._nn.linear(
+            x_106,
+            l_self_modules_features_modules_5_modules_4_modules_attn_modules_qkv_parameters_weight_,
+            l_self_modules_features_modules_5_modules_4_modules_attn_modules_qkv_parameters_bias_,
+        )
+        x_106 = l_self_modules_features_modules_5_modules_4_modules_attn_modules_qkv_parameters_weight_ = l_self_modules_features_modules_5_modules_4_modules_attn_modules_qkv_parameters_bias_ = (None)
+        reshape_37 = qkv_16.reshape(4, 49, 3, 16, 32)
+        qkv_16 = None
+        qkv_17 = reshape_37.permute(2, 0, 3, 1, 4)
+        reshape_37 = None
+        q_16 = qkv_17[0]
+        k_8 = qkv_17[1]
+        v_8 = qkv_17[2]
+        qkv_17 = None
+        q_17 = q_16 * 0.1767766952966369
+        q_16 = None
+        transpose_16 = k_8.transpose(-2, -1)
+        k_8 = None
+        attn_44 = q_17.matmul(transpose_16)
+        q_17 = transpose_16 = None
+        attn_45 = attn_44 + relative_position_bias_26
+        attn_44 = relative_position_bias_26 = None
+        attn_46 = torch.nn.functional.softmax(attn_45, dim=-1)
+        attn_45 = None
+        attn_47 = torch.nn.functional.dropout(attn_46, p=0.0, training=False)
+        attn_46 = None
+        matmul_17 = attn_47.matmul(v_8)
+        attn_47 = v_8 = None
+        transpose_17 = matmul_17.transpose(1, 2)
+        matmul_17 = None
+        x_107 = transpose_17.reshape(4, 49, 512)
+        transpose_17 = None
+        x_108 = torch._C._nn.linear(
+            x_107,
+            l_self_modules_features_modules_5_modules_4_modules_attn_modules_proj_parameters_weight_,
+            l_self_modules_features_modules_5_modules_4_modules_attn_modules_proj_parameters_bias_,
+        )
+        x_107 = l_self_modules_features_modules_5_modules_4_modules_attn_modules_proj_parameters_weight_ = l_self_modules_features_modules_5_modules_4_modules_attn_modules_proj_parameters_bias_ = (None)
+        x_109 = torch.nn.functional.dropout(x_108, p=0.0, training=False)
+        x_108 = None
+        x_110 = x_109.view(1, 2, 2, 7, 7, 512)
+        x_109 = None
+        permute_40 = x_110.permute(0, 1, 3, 2, 4, 5)
+        x_110 = None
+        x_111 = permute_40.reshape(1, 14, 14, 512)
+        permute_40 = None
+        getitem_52 = x_111[
+            (
+                slice(None, None, None),
+                slice(None, 14, None),
+                slice(None, 14, None),
+                slice(None, None, None),
+            )
+        ]
+        x_111 = None
+        x_112 = getitem_52.contiguous()
+        getitem_52 = None
+        _log_api_usage_once_16 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_16 = None
+        x_113 = x_103 + x_112
+        x_103 = x_112 = None
+        layer_norm_20 = torch.nn.functional.layer_norm(
+            x_113,
+            (512,),
+            l_self_modules_features_modules_5_modules_4_modules_norm2_parameters_weight_,
+            l_self_modules_features_modules_5_modules_4_modules_norm2_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_5_modules_4_modules_norm2_parameters_weight_ = (
+            l_self_modules_features_modules_5_modules_4_modules_norm2_parameters_bias_
+        ) = None
+        input_44 = torch._C._nn.linear(
+            layer_norm_20,
+            l_self_modules_features_modules_5_modules_4_modules_mlp_modules_0_parameters_weight_,
+            l_self_modules_features_modules_5_modules_4_modules_mlp_modules_0_parameters_bias_,
+        )
+        layer_norm_20 = l_self_modules_features_modules_5_modules_4_modules_mlp_modules_0_parameters_weight_ = l_self_modules_features_modules_5_modules_4_modules_mlp_modules_0_parameters_bias_ = (None)
+        input_45 = torch._C._nn.gelu(input_44, approximate="none")
+        input_44 = None
+        input_46 = torch.nn.functional.dropout(input_45, 0.0, False, False)
+        input_45 = None
+        input_47 = torch._C._nn.linear(
+            input_46,
+            l_self_modules_features_modules_5_modules_4_modules_mlp_modules_3_parameters_weight_,
+            l_self_modules_features_modules_5_modules_4_modules_mlp_modules_3_parameters_bias_,
+        )
+        input_46 = l_self_modules_features_modules_5_modules_4_modules_mlp_modules_3_parameters_weight_ = l_self_modules_features_modules_5_modules_4_modules_mlp_modules_3_parameters_bias_ = (None)
+        input_48 = torch.nn.functional.dropout(input_47, 0.0, False, False)
+        input_47 = None
+        _log_api_usage_once_17 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_17 = None
+        x_114 = x_113 + input_48
+        x_113 = input_48 = None
+        layer_norm_21 = torch.nn.functional.layer_norm(
+            x_114,
+            (512,),
+            l_self_modules_features_modules_5_modules_5_modules_norm1_parameters_weight_,
+            l_self_modules_features_modules_5_modules_5_modules_norm1_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_5_modules_5_modules_norm1_parameters_weight_ = (
+            l_self_modules_features_modules_5_modules_5_modules_norm1_parameters_bias_
+        ) = None
+        relative_position_bias_27 = l_self_modules_features_modules_5_modules_5_modules_attn_parameters_relative_position_bias_table_[
+            l_self_modules_features_modules_5_modules_5_modules_attn_buffers_relative_position_index_
+        ]
+        l_self_modules_features_modules_5_modules_5_modules_attn_parameters_relative_position_bias_table_ = l_self_modules_features_modules_5_modules_5_modules_attn_buffers_relative_position_index_ = (None)
+        relative_position_bias_28 = relative_position_bias_27.view(49, 49, -1)
+        relative_position_bias_27 = None
+        permute_41 = relative_position_bias_28.permute(2, 0, 1)
+        relative_position_bias_28 = None
+        contiguous_18 = permute_41.contiguous()
+        permute_41 = None
+        relative_position_bias_29 = contiguous_18.unsqueeze(0)
+        contiguous_18 = None
+        x_115 = torch._C._nn.pad(layer_norm_21, (0, 0, 0, 0, 0, 0), "constant", None)
+        layer_norm_21 = None
+        x_116 = torch.roll(x_115, shifts=(-3, -3), dims=(1, 2))
+        x_115 = None
+        x_117 = x_116.view(1, 2, 7, 2, 7, 512)
+        x_116 = None
+        permute_42 = x_117.permute(0, 1, 3, 2, 4, 5)
+        x_117 = None
+        x_118 = permute_42.reshape(4, 49, 512)
+        permute_42 = None
+        qkv_18 = torch._C._nn.linear(
+            x_118,
+            l_self_modules_features_modules_5_modules_5_modules_attn_modules_qkv_parameters_weight_,
+            l_self_modules_features_modules_5_modules_5_modules_attn_modules_qkv_parameters_bias_,
+        )
+        l_self_modules_features_modules_5_modules_5_modules_attn_modules_qkv_parameters_weight_ = l_self_modules_features_modules_5_modules_5_modules_attn_modules_qkv_parameters_bias_ = (None)
+        reshape_41 = qkv_18.reshape(4, 49, 3, 16, 32)
+        qkv_18 = None
+        qkv_19 = reshape_41.permute(2, 0, 3, 1, 4)
+        reshape_41 = None
+        q_18 = qkv_19[0]
+        k_9 = qkv_19[1]
+        v_9 = qkv_19[2]
+        qkv_19 = None
+        q_19 = q_18 * 0.1767766952966369
+        q_18 = None
+        transpose_18 = k_9.transpose(-2, -1)
+        k_9 = None
+        attn_48 = q_19.matmul(transpose_18)
+        q_19 = transpose_18 = None
+        attn_49 = attn_48 + relative_position_bias_29
+        attn_48 = relative_position_bias_29 = None
+        attn_mask_20 = x_118.new_zeros((14, 14))
+        x_118 = None
+        attn_mask_20[(slice(0, -7, None), slice(0, -7, None))] = 0
+        setitem_36 = attn_mask_20
+        setitem_36 = None
+        attn_mask_20[(slice(0, -7, None), slice(-7, -3, None))] = 1
+        setitem_37 = attn_mask_20
+        setitem_37 = None
+        attn_mask_20[(slice(0, -7, None), slice(-3, None, None))] = 2
+        setitem_38 = attn_mask_20
+        setitem_38 = None
+        attn_mask_20[(slice(-7, -3, None), slice(0, -7, None))] = 3
+        setitem_39 = attn_mask_20
+        setitem_39 = None
+        attn_mask_20[(slice(-7, -3, None), slice(-7, -3, None))] = 4
+        setitem_40 = attn_mask_20
+        setitem_40 = None
+        attn_mask_20[(slice(-7, -3, None), slice(-3, None, None))] = 5
+        setitem_41 = attn_mask_20
+        setitem_41 = None
+        attn_mask_20[(slice(-3, None, None), slice(0, -7, None))] = 6
+        setitem_42 = attn_mask_20
+        setitem_42 = None
+        attn_mask_20[(slice(-3, None, None), slice(-7, -3, None))] = 7
+        setitem_43 = attn_mask_20
+        setitem_43 = None
+        attn_mask_20[(slice(-3, None, None), slice(-3, None, None))] = 8
+        setitem_44 = attn_mask_20
+        setitem_44 = None
+        attn_mask_21 = attn_mask_20.view(2, 7, 2, 7)
+        attn_mask_20 = None
+        permute_44 = attn_mask_21.permute(0, 2, 1, 3)
+        attn_mask_21 = None
+        attn_mask_22 = permute_44.reshape(4, 49)
+        permute_44 = None
+        unsqueeze_26 = attn_mask_22.unsqueeze(1)
+        unsqueeze_27 = attn_mask_22.unsqueeze(2)
+        attn_mask_22 = None
+        attn_mask_23 = unsqueeze_26 - unsqueeze_27
+        unsqueeze_26 = unsqueeze_27 = None
+        ne_4 = attn_mask_23 != 0
+        masked_fill_8 = attn_mask_23.masked_fill(ne_4, -100.0)
+        ne_4 = None
+        eq_4 = attn_mask_23 == 0
+        attn_mask_23 = None
+        attn_mask_24 = masked_fill_8.masked_fill(eq_4, 0.0)
+        masked_fill_8 = eq_4 = None
+        attn_50 = attn_49.view(1, 4, 16, 49, 49)
+        attn_49 = None
+        unsqueeze_28 = attn_mask_24.unsqueeze(1)
+        attn_mask_24 = None
+        unsqueeze_29 = unsqueeze_28.unsqueeze(0)
+        unsqueeze_28 = None
+        attn_51 = attn_50 + unsqueeze_29
+        attn_50 = unsqueeze_29 = None
+        attn_52 = attn_51.view(-1, 16, 49, 49)
+        attn_51 = None
+        attn_53 = torch.nn.functional.softmax(attn_52, dim=-1)
+        attn_52 = None
+        attn_54 = torch.nn.functional.dropout(attn_53, p=0.0, training=False)
+        attn_53 = None
+        matmul_19 = attn_54.matmul(v_9)
+        attn_54 = v_9 = None
+        transpose_19 = matmul_19.transpose(1, 2)
+        matmul_19 = None
+        x_119 = transpose_19.reshape(4, 49, 512)
+        transpose_19 = None
+        x_120 = torch._C._nn.linear(
+            x_119,
+            l_self_modules_features_modules_5_modules_5_modules_attn_modules_proj_parameters_weight_,
+            l_self_modules_features_modules_5_modules_5_modules_attn_modules_proj_parameters_bias_,
+        )
+        x_119 = l_self_modules_features_modules_5_modules_5_modules_attn_modules_proj_parameters_weight_ = l_self_modules_features_modules_5_modules_5_modules_attn_modules_proj_parameters_bias_ = (None)
+        x_121 = torch.nn.functional.dropout(x_120, p=0.0, training=False)
+        x_120 = None
+        x_122 = x_121.view(1, 2, 2, 7, 7, 512)
+        x_121 = None
+        permute_45 = x_122.permute(0, 1, 3, 2, 4, 5)
+        x_122 = None
+        x_123 = permute_45.reshape(1, 14, 14, 512)
+        permute_45 = None
+        x_124 = torch.roll(x_123, shifts=(3, 3), dims=(1, 2))
+        x_123 = None
+        getitem_57 = x_124[
+            (
+                slice(None, None, None),
+                slice(None, 14, None),
+                slice(None, 14, None),
+                slice(None, None, None),
+            )
+        ]
+        x_124 = None
+        x_125 = getitem_57.contiguous()
+        getitem_57 = None
+        _log_api_usage_once_18 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_18 = None
+        x_126 = x_114 + x_125
+        x_114 = x_125 = None
+        layer_norm_22 = torch.nn.functional.layer_norm(
+            x_126,
+            (512,),
+            l_self_modules_features_modules_5_modules_5_modules_norm2_parameters_weight_,
+            l_self_modules_features_modules_5_modules_5_modules_norm2_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_5_modules_5_modules_norm2_parameters_weight_ = (
+            l_self_modules_features_modules_5_modules_5_modules_norm2_parameters_bias_
+        ) = None
+        input_49 = torch._C._nn.linear(
+            layer_norm_22,
+            l_self_modules_features_modules_5_modules_5_modules_mlp_modules_0_parameters_weight_,
+            l_self_modules_features_modules_5_modules_5_modules_mlp_modules_0_parameters_bias_,
+        )
+        layer_norm_22 = l_self_modules_features_modules_5_modules_5_modules_mlp_modules_0_parameters_weight_ = l_self_modules_features_modules_5_modules_5_modules_mlp_modules_0_parameters_bias_ = (None)
+        input_50 = torch._C._nn.gelu(input_49, approximate="none")
+        input_49 = None
+        input_51 = torch.nn.functional.dropout(input_50, 0.0, False, False)
+        input_50 = None
+        input_52 = torch._C._nn.linear(
+            input_51,
+            l_self_modules_features_modules_5_modules_5_modules_mlp_modules_3_parameters_weight_,
+            l_self_modules_features_modules_5_modules_5_modules_mlp_modules_3_parameters_bias_,
+        )
+        input_51 = l_self_modules_features_modules_5_modules_5_modules_mlp_modules_3_parameters_weight_ = l_self_modules_features_modules_5_modules_5_modules_mlp_modules_3_parameters_bias_ = (None)
+        input_53 = torch.nn.functional.dropout(input_52, 0.0, False, False)
+        input_52 = None
+        _log_api_usage_once_19 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_19 = None
+        x_127 = x_126 + input_53
+        x_126 = input_53 = None
+        layer_norm_23 = torch.nn.functional.layer_norm(
+            x_127,
+            (512,),
+            l_self_modules_features_modules_5_modules_6_modules_norm1_parameters_weight_,
+            l_self_modules_features_modules_5_modules_6_modules_norm1_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_5_modules_6_modules_norm1_parameters_weight_ = (
+            l_self_modules_features_modules_5_modules_6_modules_norm1_parameters_bias_
+        ) = None
+        relative_position_bias_30 = l_self_modules_features_modules_5_modules_6_modules_attn_parameters_relative_position_bias_table_[
+            l_self_modules_features_modules_5_modules_6_modules_attn_buffers_relative_position_index_
+        ]
+        l_self_modules_features_modules_5_modules_6_modules_attn_parameters_relative_position_bias_table_ = l_self_modules_features_modules_5_modules_6_modules_attn_buffers_relative_position_index_ = (None)
+        relative_position_bias_31 = relative_position_bias_30.view(49, 49, -1)
+        relative_position_bias_30 = None
+        permute_46 = relative_position_bias_31.permute(2, 0, 1)
+        relative_position_bias_31 = None
+        contiguous_20 = permute_46.contiguous()
+        permute_46 = None
+        relative_position_bias_32 = contiguous_20.unsqueeze(0)
+        contiguous_20 = None
+        x_128 = torch._C._nn.pad(layer_norm_23, (0, 0, 0, 0, 0, 0), "constant", None)
+        layer_norm_23 = None
+        x_129 = x_128.view(1, 2, 7, 2, 7, 512)
+        x_128 = None
+        permute_47 = x_129.permute(0, 1, 3, 2, 4, 5)
+        x_129 = None
+        x_130 = permute_47.reshape(4, 49, 512)
+        permute_47 = None
+        qkv_20 = torch._C._nn.linear(
+            x_130,
+            l_self_modules_features_modules_5_modules_6_modules_attn_modules_qkv_parameters_weight_,
+            l_self_modules_features_modules_5_modules_6_modules_attn_modules_qkv_parameters_bias_,
+        )
+        x_130 = l_self_modules_features_modules_5_modules_6_modules_attn_modules_qkv_parameters_weight_ = l_self_modules_features_modules_5_modules_6_modules_attn_modules_qkv_parameters_bias_ = (None)
+        reshape_46 = qkv_20.reshape(4, 49, 3, 16, 32)
+        qkv_20 = None
+        qkv_21 = reshape_46.permute(2, 0, 3, 1, 4)
+        reshape_46 = None
+        q_20 = qkv_21[0]
+        k_10 = qkv_21[1]
+        v_10 = qkv_21[2]
+        qkv_21 = None
+        q_21 = q_20 * 0.1767766952966369
+        q_20 = None
+        transpose_20 = k_10.transpose(-2, -1)
+        k_10 = None
+        attn_55 = q_21.matmul(transpose_20)
+        q_21 = transpose_20 = None
+        attn_56 = attn_55 + relative_position_bias_32
+        attn_55 = relative_position_bias_32 = None
+        attn_57 = torch.nn.functional.softmax(attn_56, dim=-1)
+        attn_56 = None
+        attn_58 = torch.nn.functional.dropout(attn_57, p=0.0, training=False)
+        attn_57 = None
+        matmul_21 = attn_58.matmul(v_10)
+        attn_58 = v_10 = None
+        transpose_21 = matmul_21.transpose(1, 2)
+        matmul_21 = None
+        x_131 = transpose_21.reshape(4, 49, 512)
+        transpose_21 = None
+        x_132 = torch._C._nn.linear(
+            x_131,
+            l_self_modules_features_modules_5_modules_6_modules_attn_modules_proj_parameters_weight_,
+            l_self_modules_features_modules_5_modules_6_modules_attn_modules_proj_parameters_bias_,
+        )
+        x_131 = l_self_modules_features_modules_5_modules_6_modules_attn_modules_proj_parameters_weight_ = l_self_modules_features_modules_5_modules_6_modules_attn_modules_proj_parameters_bias_ = (None)
+        x_133 = torch.nn.functional.dropout(x_132, p=0.0, training=False)
+        x_132 = None
+        x_134 = x_133.view(1, 2, 2, 7, 7, 512)
+        x_133 = None
+        permute_49 = x_134.permute(0, 1, 3, 2, 4, 5)
+        x_134 = None
+        x_135 = permute_49.reshape(1, 14, 14, 512)
+        permute_49 = None
+        getitem_62 = x_135[
+            (
+                slice(None, None, None),
+                slice(None, 14, None),
+                slice(None, 14, None),
+                slice(None, None, None),
+            )
+        ]
+        x_135 = None
+        x_136 = getitem_62.contiguous()
+        getitem_62 = None
+        _log_api_usage_once_20 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_20 = None
+        x_137 = x_127 + x_136
+        x_127 = x_136 = None
+        layer_norm_24 = torch.nn.functional.layer_norm(
+            x_137,
+            (512,),
+            l_self_modules_features_modules_5_modules_6_modules_norm2_parameters_weight_,
+            l_self_modules_features_modules_5_modules_6_modules_norm2_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_5_modules_6_modules_norm2_parameters_weight_ = (
+            l_self_modules_features_modules_5_modules_6_modules_norm2_parameters_bias_
+        ) = None
+        input_54 = torch._C._nn.linear(
+            layer_norm_24,
+            l_self_modules_features_modules_5_modules_6_modules_mlp_modules_0_parameters_weight_,
+            l_self_modules_features_modules_5_modules_6_modules_mlp_modules_0_parameters_bias_,
+        )
+        layer_norm_24 = l_self_modules_features_modules_5_modules_6_modules_mlp_modules_0_parameters_weight_ = l_self_modules_features_modules_5_modules_6_modules_mlp_modules_0_parameters_bias_ = (None)
+        input_55 = torch._C._nn.gelu(input_54, approximate="none")
+        input_54 = None
+        input_56 = torch.nn.functional.dropout(input_55, 0.0, False, False)
+        input_55 = None
+        input_57 = torch._C._nn.linear(
+            input_56,
+            l_self_modules_features_modules_5_modules_6_modules_mlp_modules_3_parameters_weight_,
+            l_self_modules_features_modules_5_modules_6_modules_mlp_modules_3_parameters_bias_,
+        )
+        input_56 = l_self_modules_features_modules_5_modules_6_modules_mlp_modules_3_parameters_weight_ = l_self_modules_features_modules_5_modules_6_modules_mlp_modules_3_parameters_bias_ = (None)
+        input_58 = torch.nn.functional.dropout(input_57, 0.0, False, False)
+        input_57 = None
+        _log_api_usage_once_21 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_21 = None
+        x_138 = x_137 + input_58
+        x_137 = input_58 = None
+        layer_norm_25 = torch.nn.functional.layer_norm(
+            x_138,
+            (512,),
+            l_self_modules_features_modules_5_modules_7_modules_norm1_parameters_weight_,
+            l_self_modules_features_modules_5_modules_7_modules_norm1_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_5_modules_7_modules_norm1_parameters_weight_ = (
+            l_self_modules_features_modules_5_modules_7_modules_norm1_parameters_bias_
+        ) = None
+        relative_position_bias_33 = l_self_modules_features_modules_5_modules_7_modules_attn_parameters_relative_position_bias_table_[
+            l_self_modules_features_modules_5_modules_7_modules_attn_buffers_relative_position_index_
+        ]
+        l_self_modules_features_modules_5_modules_7_modules_attn_parameters_relative_position_bias_table_ = l_self_modules_features_modules_5_modules_7_modules_attn_buffers_relative_position_index_ = (None)
+        relative_position_bias_34 = relative_position_bias_33.view(49, 49, -1)
+        relative_position_bias_33 = None
+        permute_50 = relative_position_bias_34.permute(2, 0, 1)
+        relative_position_bias_34 = None
+        contiguous_22 = permute_50.contiguous()
+        permute_50 = None
+        relative_position_bias_35 = contiguous_22.unsqueeze(0)
+        contiguous_22 = None
+        x_139 = torch._C._nn.pad(layer_norm_25, (0, 0, 0, 0, 0, 0), "constant", None)
+        layer_norm_25 = None
+        x_140 = torch.roll(x_139, shifts=(-3, -3), dims=(1, 2))
+        x_139 = None
+        x_141 = x_140.view(1, 2, 7, 2, 7, 512)
+        x_140 = None
+        permute_51 = x_141.permute(0, 1, 3, 2, 4, 5)
+        x_141 = None
+        x_142 = permute_51.reshape(4, 49, 512)
+        permute_51 = None
+        qkv_22 = torch._C._nn.linear(
+            x_142,
+            l_self_modules_features_modules_5_modules_7_modules_attn_modules_qkv_parameters_weight_,
+            l_self_modules_features_modules_5_modules_7_modules_attn_modules_qkv_parameters_bias_,
+        )
+        l_self_modules_features_modules_5_modules_7_modules_attn_modules_qkv_parameters_weight_ = l_self_modules_features_modules_5_modules_7_modules_attn_modules_qkv_parameters_bias_ = (None)
+        reshape_50 = qkv_22.reshape(4, 49, 3, 16, 32)
+        qkv_22 = None
+        qkv_23 = reshape_50.permute(2, 0, 3, 1, 4)
+        reshape_50 = None
+        q_22 = qkv_23[0]
+        k_11 = qkv_23[1]
+        v_11 = qkv_23[2]
+        qkv_23 = None
+        q_23 = q_22 * 0.1767766952966369
+        q_22 = None
+        transpose_22 = k_11.transpose(-2, -1)
+        k_11 = None
+        attn_59 = q_23.matmul(transpose_22)
+        q_23 = transpose_22 = None
+        attn_60 = attn_59 + relative_position_bias_35
+        attn_59 = relative_position_bias_35 = None
+        attn_mask_25 = x_142.new_zeros((14, 14))
+        x_142 = None
+        attn_mask_25[(slice(0, -7, None), slice(0, -7, None))] = 0
+        setitem_45 = attn_mask_25
+        setitem_45 = None
+        attn_mask_25[(slice(0, -7, None), slice(-7, -3, None))] = 1
+        setitem_46 = attn_mask_25
+        setitem_46 = None
+        attn_mask_25[(slice(0, -7, None), slice(-3, None, None))] = 2
+        setitem_47 = attn_mask_25
+        setitem_47 = None
+        attn_mask_25[(slice(-7, -3, None), slice(0, -7, None))] = 3
+        setitem_48 = attn_mask_25
+        setitem_48 = None
+        attn_mask_25[(slice(-7, -3, None), slice(-7, -3, None))] = 4
+        setitem_49 = attn_mask_25
+        setitem_49 = None
+        attn_mask_25[(slice(-7, -3, None), slice(-3, None, None))] = 5
+        setitem_50 = attn_mask_25
+        setitem_50 = None
+        attn_mask_25[(slice(-3, None, None), slice(0, -7, None))] = 6
+        setitem_51 = attn_mask_25
+        setitem_51 = None
+        attn_mask_25[(slice(-3, None, None), slice(-7, -3, None))] = 7
+        setitem_52 = attn_mask_25
+        setitem_52 = None
+        attn_mask_25[(slice(-3, None, None), slice(-3, None, None))] = 8
+        setitem_53 = attn_mask_25
+        setitem_53 = None
+        attn_mask_26 = attn_mask_25.view(2, 7, 2, 7)
+        attn_mask_25 = None
+        permute_53 = attn_mask_26.permute(0, 2, 1, 3)
+        attn_mask_26 = None
+        attn_mask_27 = permute_53.reshape(4, 49)
+        permute_53 = None
+        unsqueeze_32 = attn_mask_27.unsqueeze(1)
+        unsqueeze_33 = attn_mask_27.unsqueeze(2)
+        attn_mask_27 = None
+        attn_mask_28 = unsqueeze_32 - unsqueeze_33
+        unsqueeze_32 = unsqueeze_33 = None
+        ne_5 = attn_mask_28 != 0
+        masked_fill_10 = attn_mask_28.masked_fill(ne_5, -100.0)
+        ne_5 = None
+        eq_5 = attn_mask_28 == 0
+        attn_mask_28 = None
+        attn_mask_29 = masked_fill_10.masked_fill(eq_5, 0.0)
+        masked_fill_10 = eq_5 = None
+        attn_61 = attn_60.view(1, 4, 16, 49, 49)
+        attn_60 = None
+        unsqueeze_34 = attn_mask_29.unsqueeze(1)
+        attn_mask_29 = None
+        unsqueeze_35 = unsqueeze_34.unsqueeze(0)
+        unsqueeze_34 = None
+        attn_62 = attn_61 + unsqueeze_35
+        attn_61 = unsqueeze_35 = None
+        attn_63 = attn_62.view(-1, 16, 49, 49)
+        attn_62 = None
+        attn_64 = torch.nn.functional.softmax(attn_63, dim=-1)
+        attn_63 = None
+        attn_65 = torch.nn.functional.dropout(attn_64, p=0.0, training=False)
+        attn_64 = None
+        matmul_23 = attn_65.matmul(v_11)
+        attn_65 = v_11 = None
+        transpose_23 = matmul_23.transpose(1, 2)
+        matmul_23 = None
+        x_143 = transpose_23.reshape(4, 49, 512)
+        transpose_23 = None
+        x_144 = torch._C._nn.linear(
+            x_143,
+            l_self_modules_features_modules_5_modules_7_modules_attn_modules_proj_parameters_weight_,
+            l_self_modules_features_modules_5_modules_7_modules_attn_modules_proj_parameters_bias_,
+        )
+        x_143 = l_self_modules_features_modules_5_modules_7_modules_attn_modules_proj_parameters_weight_ = l_self_modules_features_modules_5_modules_7_modules_attn_modules_proj_parameters_bias_ = (None)
+        x_145 = torch.nn.functional.dropout(x_144, p=0.0, training=False)
+        x_144 = None
+        x_146 = x_145.view(1, 2, 2, 7, 7, 512)
+        x_145 = None
+        permute_54 = x_146.permute(0, 1, 3, 2, 4, 5)
+        x_146 = None
+        x_147 = permute_54.reshape(1, 14, 14, 512)
+        permute_54 = None
+        x_148 = torch.roll(x_147, shifts=(3, 3), dims=(1, 2))
+        x_147 = None
+        getitem_67 = x_148[
+            (
+                slice(None, None, None),
+                slice(None, 14, None),
+                slice(None, 14, None),
+                slice(None, None, None),
+            )
+        ]
+        x_148 = None
+        x_149 = getitem_67.contiguous()
+        getitem_67 = None
+        _log_api_usage_once_22 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_22 = None
+        x_150 = x_138 + x_149
+        x_138 = x_149 = None
+        layer_norm_26 = torch.nn.functional.layer_norm(
+            x_150,
+            (512,),
+            l_self_modules_features_modules_5_modules_7_modules_norm2_parameters_weight_,
+            l_self_modules_features_modules_5_modules_7_modules_norm2_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_5_modules_7_modules_norm2_parameters_weight_ = (
+            l_self_modules_features_modules_5_modules_7_modules_norm2_parameters_bias_
+        ) = None
+        input_59 = torch._C._nn.linear(
+            layer_norm_26,
+            l_self_modules_features_modules_5_modules_7_modules_mlp_modules_0_parameters_weight_,
+            l_self_modules_features_modules_5_modules_7_modules_mlp_modules_0_parameters_bias_,
+        )
+        layer_norm_26 = l_self_modules_features_modules_5_modules_7_modules_mlp_modules_0_parameters_weight_ = l_self_modules_features_modules_5_modules_7_modules_mlp_modules_0_parameters_bias_ = (None)
+        input_60 = torch._C._nn.gelu(input_59, approximate="none")
+        input_59 = None
+        input_61 = torch.nn.functional.dropout(input_60, 0.0, False, False)
+        input_60 = None
+        input_62 = torch._C._nn.linear(
+            input_61,
+            l_self_modules_features_modules_5_modules_7_modules_mlp_modules_3_parameters_weight_,
+            l_self_modules_features_modules_5_modules_7_modules_mlp_modules_3_parameters_bias_,
+        )
+        input_61 = l_self_modules_features_modules_5_modules_7_modules_mlp_modules_3_parameters_weight_ = l_self_modules_features_modules_5_modules_7_modules_mlp_modules_3_parameters_bias_ = (None)
+        input_63 = torch.nn.functional.dropout(input_62, 0.0, False, False)
+        input_62 = None
+        _log_api_usage_once_23 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_23 = None
+        x_151 = x_150 + input_63
+        x_150 = input_63 = None
+        layer_norm_27 = torch.nn.functional.layer_norm(
+            x_151,
+            (512,),
+            l_self_modules_features_modules_5_modules_8_modules_norm1_parameters_weight_,
+            l_self_modules_features_modules_5_modules_8_modules_norm1_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_5_modules_8_modules_norm1_parameters_weight_ = (
+            l_self_modules_features_modules_5_modules_8_modules_norm1_parameters_bias_
+        ) = None
+        relative_position_bias_36 = l_self_modules_features_modules_5_modules_8_modules_attn_parameters_relative_position_bias_table_[
+            l_self_modules_features_modules_5_modules_8_modules_attn_buffers_relative_position_index_
+        ]
+        l_self_modules_features_modules_5_modules_8_modules_attn_parameters_relative_position_bias_table_ = l_self_modules_features_modules_5_modules_8_modules_attn_buffers_relative_position_index_ = (None)
+        relative_position_bias_37 = relative_position_bias_36.view(49, 49, -1)
+        relative_position_bias_36 = None
+        permute_55 = relative_position_bias_37.permute(2, 0, 1)
+        relative_position_bias_37 = None
+        contiguous_24 = permute_55.contiguous()
+        permute_55 = None
+        relative_position_bias_38 = contiguous_24.unsqueeze(0)
+        contiguous_24 = None
+        x_152 = torch._C._nn.pad(layer_norm_27, (0, 0, 0, 0, 0, 0), "constant", None)
+        layer_norm_27 = None
+        x_153 = x_152.view(1, 2, 7, 2, 7, 512)
+        x_152 = None
+        permute_56 = x_153.permute(0, 1, 3, 2, 4, 5)
+        x_153 = None
+        x_154 = permute_56.reshape(4, 49, 512)
+        permute_56 = None
+        qkv_24 = torch._C._nn.linear(
+            x_154,
+            l_self_modules_features_modules_5_modules_8_modules_attn_modules_qkv_parameters_weight_,
+            l_self_modules_features_modules_5_modules_8_modules_attn_modules_qkv_parameters_bias_,
+        )
+        x_154 = l_self_modules_features_modules_5_modules_8_modules_attn_modules_qkv_parameters_weight_ = l_self_modules_features_modules_5_modules_8_modules_attn_modules_qkv_parameters_bias_ = (None)
+        reshape_55 = qkv_24.reshape(4, 49, 3, 16, 32)
+        qkv_24 = None
+        qkv_25 = reshape_55.permute(2, 0, 3, 1, 4)
+        reshape_55 = None
+        q_24 = qkv_25[0]
+        k_12 = qkv_25[1]
+        v_12 = qkv_25[2]
+        qkv_25 = None
+        q_25 = q_24 * 0.1767766952966369
+        q_24 = None
+        transpose_24 = k_12.transpose(-2, -1)
+        k_12 = None
+        attn_66 = q_25.matmul(transpose_24)
+        q_25 = transpose_24 = None
+        attn_67 = attn_66 + relative_position_bias_38
+        attn_66 = relative_position_bias_38 = None
+        attn_68 = torch.nn.functional.softmax(attn_67, dim=-1)
+        attn_67 = None
+        attn_69 = torch.nn.functional.dropout(attn_68, p=0.0, training=False)
+        attn_68 = None
+        matmul_25 = attn_69.matmul(v_12)
+        attn_69 = v_12 = None
+        transpose_25 = matmul_25.transpose(1, 2)
+        matmul_25 = None
+        x_155 = transpose_25.reshape(4, 49, 512)
+        transpose_25 = None
+        x_156 = torch._C._nn.linear(
+            x_155,
+            l_self_modules_features_modules_5_modules_8_modules_attn_modules_proj_parameters_weight_,
+            l_self_modules_features_modules_5_modules_8_modules_attn_modules_proj_parameters_bias_,
+        )
+        x_155 = l_self_modules_features_modules_5_modules_8_modules_attn_modules_proj_parameters_weight_ = l_self_modules_features_modules_5_modules_8_modules_attn_modules_proj_parameters_bias_ = (None)
+        x_157 = torch.nn.functional.dropout(x_156, p=0.0, training=False)
+        x_156 = None
+        x_158 = x_157.view(1, 2, 2, 7, 7, 512)
+        x_157 = None
+        permute_58 = x_158.permute(0, 1, 3, 2, 4, 5)
+        x_158 = None
+        x_159 = permute_58.reshape(1, 14, 14, 512)
+        permute_58 = None
+        getitem_72 = x_159[
+            (
+                slice(None, None, None),
+                slice(None, 14, None),
+                slice(None, 14, None),
+                slice(None, None, None),
+            )
+        ]
+        x_159 = None
+        x_160 = getitem_72.contiguous()
+        getitem_72 = None
+        _log_api_usage_once_24 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_24 = None
+        x_161 = x_151 + x_160
+        x_151 = x_160 = None
+        layer_norm_28 = torch.nn.functional.layer_norm(
+            x_161,
+            (512,),
+            l_self_modules_features_modules_5_modules_8_modules_norm2_parameters_weight_,
+            l_self_modules_features_modules_5_modules_8_modules_norm2_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_5_modules_8_modules_norm2_parameters_weight_ = (
+            l_self_modules_features_modules_5_modules_8_modules_norm2_parameters_bias_
+        ) = None
+        input_64 = torch._C._nn.linear(
+            layer_norm_28,
+            l_self_modules_features_modules_5_modules_8_modules_mlp_modules_0_parameters_weight_,
+            l_self_modules_features_modules_5_modules_8_modules_mlp_modules_0_parameters_bias_,
+        )
+        layer_norm_28 = l_self_modules_features_modules_5_modules_8_modules_mlp_modules_0_parameters_weight_ = l_self_modules_features_modules_5_modules_8_modules_mlp_modules_0_parameters_bias_ = (None)
+        input_65 = torch._C._nn.gelu(input_64, approximate="none")
+        input_64 = None
+        input_66 = torch.nn.functional.dropout(input_65, 0.0, False, False)
+        input_65 = None
+        input_67 = torch._C._nn.linear(
+            input_66,
+            l_self_modules_features_modules_5_modules_8_modules_mlp_modules_3_parameters_weight_,
+            l_self_modules_features_modules_5_modules_8_modules_mlp_modules_3_parameters_bias_,
+        )
+        input_66 = l_self_modules_features_modules_5_modules_8_modules_mlp_modules_3_parameters_weight_ = l_self_modules_features_modules_5_modules_8_modules_mlp_modules_3_parameters_bias_ = (None)
+        input_68 = torch.nn.functional.dropout(input_67, 0.0, False, False)
+        input_67 = None
+        _log_api_usage_once_25 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_25 = None
+        x_162 = x_161 + input_68
+        x_161 = input_68 = None
+        layer_norm_29 = torch.nn.functional.layer_norm(
+            x_162,
+            (512,),
+            l_self_modules_features_modules_5_modules_9_modules_norm1_parameters_weight_,
+            l_self_modules_features_modules_5_modules_9_modules_norm1_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_5_modules_9_modules_norm1_parameters_weight_ = (
+            l_self_modules_features_modules_5_modules_9_modules_norm1_parameters_bias_
+        ) = None
+        relative_position_bias_39 = l_self_modules_features_modules_5_modules_9_modules_attn_parameters_relative_position_bias_table_[
+            l_self_modules_features_modules_5_modules_9_modules_attn_buffers_relative_position_index_
+        ]
+        l_self_modules_features_modules_5_modules_9_modules_attn_parameters_relative_position_bias_table_ = l_self_modules_features_modules_5_modules_9_modules_attn_buffers_relative_position_index_ = (None)
+        relative_position_bias_40 = relative_position_bias_39.view(49, 49, -1)
+        relative_position_bias_39 = None
+        permute_59 = relative_position_bias_40.permute(2, 0, 1)
+        relative_position_bias_40 = None
+        contiguous_26 = permute_59.contiguous()
+        permute_59 = None
+        relative_position_bias_41 = contiguous_26.unsqueeze(0)
+        contiguous_26 = None
+        x_163 = torch._C._nn.pad(layer_norm_29, (0, 0, 0, 0, 0, 0), "constant", None)
+        layer_norm_29 = None
+        x_164 = torch.roll(x_163, shifts=(-3, -3), dims=(1, 2))
+        x_163 = None
+        x_165 = x_164.view(1, 2, 7, 2, 7, 512)
+        x_164 = None
+        permute_60 = x_165.permute(0, 1, 3, 2, 4, 5)
+        x_165 = None
+        x_166 = permute_60.reshape(4, 49, 512)
+        permute_60 = None
+        qkv_26 = torch._C._nn.linear(
+            x_166,
+            l_self_modules_features_modules_5_modules_9_modules_attn_modules_qkv_parameters_weight_,
+            l_self_modules_features_modules_5_modules_9_modules_attn_modules_qkv_parameters_bias_,
+        )
+        l_self_modules_features_modules_5_modules_9_modules_attn_modules_qkv_parameters_weight_ = l_self_modules_features_modules_5_modules_9_modules_attn_modules_qkv_parameters_bias_ = (None)
+        reshape_59 = qkv_26.reshape(4, 49, 3, 16, 32)
+        qkv_26 = None
+        qkv_27 = reshape_59.permute(2, 0, 3, 1, 4)
+        reshape_59 = None
+        q_26 = qkv_27[0]
+        k_13 = qkv_27[1]
+        v_13 = qkv_27[2]
+        qkv_27 = None
+        q_27 = q_26 * 0.1767766952966369
+        q_26 = None
+        transpose_26 = k_13.transpose(-2, -1)
+        k_13 = None
+        attn_70 = q_27.matmul(transpose_26)
+        q_27 = transpose_26 = None
+        attn_71 = attn_70 + relative_position_bias_41
+        attn_70 = relative_position_bias_41 = None
+        attn_mask_30 = x_166.new_zeros((14, 14))
+        x_166 = None
+        attn_mask_30[(slice(0, -7, None), slice(0, -7, None))] = 0
+        setitem_54 = attn_mask_30
+        setitem_54 = None
+        attn_mask_30[(slice(0, -7, None), slice(-7, -3, None))] = 1
+        setitem_55 = attn_mask_30
+        setitem_55 = None
+        attn_mask_30[(slice(0, -7, None), slice(-3, None, None))] = 2
+        setitem_56 = attn_mask_30
+        setitem_56 = None
+        attn_mask_30[(slice(-7, -3, None), slice(0, -7, None))] = 3
+        setitem_57 = attn_mask_30
+        setitem_57 = None
+        attn_mask_30[(slice(-7, -3, None), slice(-7, -3, None))] = 4
+        setitem_58 = attn_mask_30
+        setitem_58 = None
+        attn_mask_30[(slice(-7, -3, None), slice(-3, None, None))] = 5
+        setitem_59 = attn_mask_30
+        setitem_59 = None
+        attn_mask_30[(slice(-3, None, None), slice(0, -7, None))] = 6
+        setitem_60 = attn_mask_30
+        setitem_60 = None
+        attn_mask_30[(slice(-3, None, None), slice(-7, -3, None))] = 7
+        setitem_61 = attn_mask_30
+        setitem_61 = None
+        attn_mask_30[(slice(-3, None, None), slice(-3, None, None))] = 8
+        setitem_62 = attn_mask_30
+        setitem_62 = None
+        attn_mask_31 = attn_mask_30.view(2, 7, 2, 7)
+        attn_mask_30 = None
+        permute_62 = attn_mask_31.permute(0, 2, 1, 3)
+        attn_mask_31 = None
+        attn_mask_32 = permute_62.reshape(4, 49)
+        permute_62 = None
+        unsqueeze_38 = attn_mask_32.unsqueeze(1)
+        unsqueeze_39 = attn_mask_32.unsqueeze(2)
+        attn_mask_32 = None
+        attn_mask_33 = unsqueeze_38 - unsqueeze_39
+        unsqueeze_38 = unsqueeze_39 = None
+        ne_6 = attn_mask_33 != 0
+        masked_fill_12 = attn_mask_33.masked_fill(ne_6, -100.0)
+        ne_6 = None
+        eq_6 = attn_mask_33 == 0
+        attn_mask_33 = None
+        attn_mask_34 = masked_fill_12.masked_fill(eq_6, 0.0)
+        masked_fill_12 = eq_6 = None
+        attn_72 = attn_71.view(1, 4, 16, 49, 49)
+        attn_71 = None
+        unsqueeze_40 = attn_mask_34.unsqueeze(1)
+        attn_mask_34 = None
+        unsqueeze_41 = unsqueeze_40.unsqueeze(0)
+        unsqueeze_40 = None
+        attn_73 = attn_72 + unsqueeze_41
+        attn_72 = unsqueeze_41 = None
+        attn_74 = attn_73.view(-1, 16, 49, 49)
+        attn_73 = None
+        attn_75 = torch.nn.functional.softmax(attn_74, dim=-1)
+        attn_74 = None
+        attn_76 = torch.nn.functional.dropout(attn_75, p=0.0, training=False)
+        attn_75 = None
+        matmul_27 = attn_76.matmul(v_13)
+        attn_76 = v_13 = None
+        transpose_27 = matmul_27.transpose(1, 2)
+        matmul_27 = None
+        x_167 = transpose_27.reshape(4, 49, 512)
+        transpose_27 = None
+        x_168 = torch._C._nn.linear(
+            x_167,
+            l_self_modules_features_modules_5_modules_9_modules_attn_modules_proj_parameters_weight_,
+            l_self_modules_features_modules_5_modules_9_modules_attn_modules_proj_parameters_bias_,
+        )
+        x_167 = l_self_modules_features_modules_5_modules_9_modules_attn_modules_proj_parameters_weight_ = l_self_modules_features_modules_5_modules_9_modules_attn_modules_proj_parameters_bias_ = (None)
+        x_169 = torch.nn.functional.dropout(x_168, p=0.0, training=False)
+        x_168 = None
+        x_170 = x_169.view(1, 2, 2, 7, 7, 512)
+        x_169 = None
+        permute_63 = x_170.permute(0, 1, 3, 2, 4, 5)
+        x_170 = None
+        x_171 = permute_63.reshape(1, 14, 14, 512)
+        permute_63 = None
+        x_172 = torch.roll(x_171, shifts=(3, 3), dims=(1, 2))
+        x_171 = None
+        getitem_77 = x_172[
+            (
+                slice(None, None, None),
+                slice(None, 14, None),
+                slice(None, 14, None),
+                slice(None, None, None),
+            )
+        ]
+        x_172 = None
+        x_173 = getitem_77.contiguous()
+        getitem_77 = None
+        _log_api_usage_once_26 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_26 = None
+        x_174 = x_162 + x_173
+        x_162 = x_173 = None
+        layer_norm_30 = torch.nn.functional.layer_norm(
+            x_174,
+            (512,),
+            l_self_modules_features_modules_5_modules_9_modules_norm2_parameters_weight_,
+            l_self_modules_features_modules_5_modules_9_modules_norm2_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_5_modules_9_modules_norm2_parameters_weight_ = (
+            l_self_modules_features_modules_5_modules_9_modules_norm2_parameters_bias_
+        ) = None
+        input_69 = torch._C._nn.linear(
+            layer_norm_30,
+            l_self_modules_features_modules_5_modules_9_modules_mlp_modules_0_parameters_weight_,
+            l_self_modules_features_modules_5_modules_9_modules_mlp_modules_0_parameters_bias_,
+        )
+        layer_norm_30 = l_self_modules_features_modules_5_modules_9_modules_mlp_modules_0_parameters_weight_ = l_self_modules_features_modules_5_modules_9_modules_mlp_modules_0_parameters_bias_ = (None)
+        input_70 = torch._C._nn.gelu(input_69, approximate="none")
+        input_69 = None
+        input_71 = torch.nn.functional.dropout(input_70, 0.0, False, False)
+        input_70 = None
+        input_72 = torch._C._nn.linear(
+            input_71,
+            l_self_modules_features_modules_5_modules_9_modules_mlp_modules_3_parameters_weight_,
+            l_self_modules_features_modules_5_modules_9_modules_mlp_modules_3_parameters_bias_,
+        )
+        input_71 = l_self_modules_features_modules_5_modules_9_modules_mlp_modules_3_parameters_weight_ = l_self_modules_features_modules_5_modules_9_modules_mlp_modules_3_parameters_bias_ = (None)
+        input_73 = torch.nn.functional.dropout(input_72, 0.0, False, False)
+        input_72 = None
+        _log_api_usage_once_27 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_27 = None
+        x_175 = x_174 + input_73
+        x_174 = input_73 = None
+        layer_norm_31 = torch.nn.functional.layer_norm(
+            x_175,
+            (512,),
+            l_self_modules_features_modules_5_modules_10_modules_norm1_parameters_weight_,
+            l_self_modules_features_modules_5_modules_10_modules_norm1_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_5_modules_10_modules_norm1_parameters_weight_ = (
+            l_self_modules_features_modules_5_modules_10_modules_norm1_parameters_bias_
+        ) = None
+        relative_position_bias_42 = l_self_modules_features_modules_5_modules_10_modules_attn_parameters_relative_position_bias_table_[
+            l_self_modules_features_modules_5_modules_10_modules_attn_buffers_relative_position_index_
+        ]
+        l_self_modules_features_modules_5_modules_10_modules_attn_parameters_relative_position_bias_table_ = l_self_modules_features_modules_5_modules_10_modules_attn_buffers_relative_position_index_ = (None)
+        relative_position_bias_43 = relative_position_bias_42.view(49, 49, -1)
+        relative_position_bias_42 = None
+        permute_64 = relative_position_bias_43.permute(2, 0, 1)
+        relative_position_bias_43 = None
+        contiguous_28 = permute_64.contiguous()
+        permute_64 = None
+        relative_position_bias_44 = contiguous_28.unsqueeze(0)
+        contiguous_28 = None
+        x_176 = torch._C._nn.pad(layer_norm_31, (0, 0, 0, 0, 0, 0), "constant", None)
+        layer_norm_31 = None
+        x_177 = x_176.view(1, 2, 7, 2, 7, 512)
+        x_176 = None
+        permute_65 = x_177.permute(0, 1, 3, 2, 4, 5)
+        x_177 = None
+        x_178 = permute_65.reshape(4, 49, 512)
+        permute_65 = None
+        qkv_28 = torch._C._nn.linear(
+            x_178,
+            l_self_modules_features_modules_5_modules_10_modules_attn_modules_qkv_parameters_weight_,
+            l_self_modules_features_modules_5_modules_10_modules_attn_modules_qkv_parameters_bias_,
+        )
+        x_178 = l_self_modules_features_modules_5_modules_10_modules_attn_modules_qkv_parameters_weight_ = l_self_modules_features_modules_5_modules_10_modules_attn_modules_qkv_parameters_bias_ = (None)
+        reshape_64 = qkv_28.reshape(4, 49, 3, 16, 32)
+        qkv_28 = None
+        qkv_29 = reshape_64.permute(2, 0, 3, 1, 4)
+        reshape_64 = None
+        q_28 = qkv_29[0]
+        k_14 = qkv_29[1]
+        v_14 = qkv_29[2]
+        qkv_29 = None
+        q_29 = q_28 * 0.1767766952966369
+        q_28 = None
+        transpose_28 = k_14.transpose(-2, -1)
+        k_14 = None
+        attn_77 = q_29.matmul(transpose_28)
+        q_29 = transpose_28 = None
+        attn_78 = attn_77 + relative_position_bias_44
+        attn_77 = relative_position_bias_44 = None
+        attn_79 = torch.nn.functional.softmax(attn_78, dim=-1)
+        attn_78 = None
+        attn_80 = torch.nn.functional.dropout(attn_79, p=0.0, training=False)
+        attn_79 = None
+        matmul_29 = attn_80.matmul(v_14)
+        attn_80 = v_14 = None
+        transpose_29 = matmul_29.transpose(1, 2)
+        matmul_29 = None
+        x_179 = transpose_29.reshape(4, 49, 512)
+        transpose_29 = None
+        x_180 = torch._C._nn.linear(
+            x_179,
+            l_self_modules_features_modules_5_modules_10_modules_attn_modules_proj_parameters_weight_,
+            l_self_modules_features_modules_5_modules_10_modules_attn_modules_proj_parameters_bias_,
+        )
+        x_179 = l_self_modules_features_modules_5_modules_10_modules_attn_modules_proj_parameters_weight_ = l_self_modules_features_modules_5_modules_10_modules_attn_modules_proj_parameters_bias_ = (None)
+        x_181 = torch.nn.functional.dropout(x_180, p=0.0, training=False)
+        x_180 = None
+        x_182 = x_181.view(1, 2, 2, 7, 7, 512)
+        x_181 = None
+        permute_67 = x_182.permute(0, 1, 3, 2, 4, 5)
+        x_182 = None
+        x_183 = permute_67.reshape(1, 14, 14, 512)
+        permute_67 = None
+        getitem_82 = x_183[
+            (
+                slice(None, None, None),
+                slice(None, 14, None),
+                slice(None, 14, None),
+                slice(None, None, None),
+            )
+        ]
+        x_183 = None
+        x_184 = getitem_82.contiguous()
+        getitem_82 = None
+        _log_api_usage_once_28 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_28 = None
+        x_185 = x_175 + x_184
+        x_175 = x_184 = None
+        layer_norm_32 = torch.nn.functional.layer_norm(
+            x_185,
+            (512,),
+            l_self_modules_features_modules_5_modules_10_modules_norm2_parameters_weight_,
+            l_self_modules_features_modules_5_modules_10_modules_norm2_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_5_modules_10_modules_norm2_parameters_weight_ = (
+            l_self_modules_features_modules_5_modules_10_modules_norm2_parameters_bias_
+        ) = None
+        input_74 = torch._C._nn.linear(
+            layer_norm_32,
+            l_self_modules_features_modules_5_modules_10_modules_mlp_modules_0_parameters_weight_,
+            l_self_modules_features_modules_5_modules_10_modules_mlp_modules_0_parameters_bias_,
+        )
+        layer_norm_32 = l_self_modules_features_modules_5_modules_10_modules_mlp_modules_0_parameters_weight_ = l_self_modules_features_modules_5_modules_10_modules_mlp_modules_0_parameters_bias_ = (None)
+        input_75 = torch._C._nn.gelu(input_74, approximate="none")
+        input_74 = None
+        input_76 = torch.nn.functional.dropout(input_75, 0.0, False, False)
+        input_75 = None
+        input_77 = torch._C._nn.linear(
+            input_76,
+            l_self_modules_features_modules_5_modules_10_modules_mlp_modules_3_parameters_weight_,
+            l_self_modules_features_modules_5_modules_10_modules_mlp_modules_3_parameters_bias_,
+        )
+        input_76 = l_self_modules_features_modules_5_modules_10_modules_mlp_modules_3_parameters_weight_ = l_self_modules_features_modules_5_modules_10_modules_mlp_modules_3_parameters_bias_ = (None)
+        input_78 = torch.nn.functional.dropout(input_77, 0.0, False, False)
+        input_77 = None
+        _log_api_usage_once_29 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_29 = None
+        x_186 = x_185 + input_78
+        x_185 = input_78 = None
+        layer_norm_33 = torch.nn.functional.layer_norm(
+            x_186,
+            (512,),
+            l_self_modules_features_modules_5_modules_11_modules_norm1_parameters_weight_,
+            l_self_modules_features_modules_5_modules_11_modules_norm1_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_5_modules_11_modules_norm1_parameters_weight_ = (
+            l_self_modules_features_modules_5_modules_11_modules_norm1_parameters_bias_
+        ) = None
+        relative_position_bias_45 = l_self_modules_features_modules_5_modules_11_modules_attn_parameters_relative_position_bias_table_[
+            l_self_modules_features_modules_5_modules_11_modules_attn_buffers_relative_position_index_
+        ]
+        l_self_modules_features_modules_5_modules_11_modules_attn_parameters_relative_position_bias_table_ = l_self_modules_features_modules_5_modules_11_modules_attn_buffers_relative_position_index_ = (None)
+        relative_position_bias_46 = relative_position_bias_45.view(49, 49, -1)
+        relative_position_bias_45 = None
+        permute_68 = relative_position_bias_46.permute(2, 0, 1)
+        relative_position_bias_46 = None
+        contiguous_30 = permute_68.contiguous()
+        permute_68 = None
+        relative_position_bias_47 = contiguous_30.unsqueeze(0)
+        contiguous_30 = None
+        x_187 = torch._C._nn.pad(layer_norm_33, (0, 0, 0, 0, 0, 0), "constant", None)
+        layer_norm_33 = None
+        x_188 = torch.roll(x_187, shifts=(-3, -3), dims=(1, 2))
+        x_187 = None
+        x_189 = x_188.view(1, 2, 7, 2, 7, 512)
+        x_188 = None
+        permute_69 = x_189.permute(0, 1, 3, 2, 4, 5)
+        x_189 = None
+        x_190 = permute_69.reshape(4, 49, 512)
+        permute_69 = None
+        qkv_30 = torch._C._nn.linear(
+            x_190,
+            l_self_modules_features_modules_5_modules_11_modules_attn_modules_qkv_parameters_weight_,
+            l_self_modules_features_modules_5_modules_11_modules_attn_modules_qkv_parameters_bias_,
+        )
+        l_self_modules_features_modules_5_modules_11_modules_attn_modules_qkv_parameters_weight_ = l_self_modules_features_modules_5_modules_11_modules_attn_modules_qkv_parameters_bias_ = (None)
+        reshape_68 = qkv_30.reshape(4, 49, 3, 16, 32)
+        qkv_30 = None
+        qkv_31 = reshape_68.permute(2, 0, 3, 1, 4)
+        reshape_68 = None
+        q_30 = qkv_31[0]
+        k_15 = qkv_31[1]
+        v_15 = qkv_31[2]
+        qkv_31 = None
+        q_31 = q_30 * 0.1767766952966369
+        q_30 = None
+        transpose_30 = k_15.transpose(-2, -1)
+        k_15 = None
+        attn_81 = q_31.matmul(transpose_30)
+        q_31 = transpose_30 = None
+        attn_82 = attn_81 + relative_position_bias_47
+        attn_81 = relative_position_bias_47 = None
+        attn_mask_35 = x_190.new_zeros((14, 14))
+        x_190 = None
+        attn_mask_35[(slice(0, -7, None), slice(0, -7, None))] = 0
+        setitem_63 = attn_mask_35
+        setitem_63 = None
+        attn_mask_35[(slice(0, -7, None), slice(-7, -3, None))] = 1
+        setitem_64 = attn_mask_35
+        setitem_64 = None
+        attn_mask_35[(slice(0, -7, None), slice(-3, None, None))] = 2
+        setitem_65 = attn_mask_35
+        setitem_65 = None
+        attn_mask_35[(slice(-7, -3, None), slice(0, -7, None))] = 3
+        setitem_66 = attn_mask_35
+        setitem_66 = None
+        attn_mask_35[(slice(-7, -3, None), slice(-7, -3, None))] = 4
+        setitem_67 = attn_mask_35
+        setitem_67 = None
+        attn_mask_35[(slice(-7, -3, None), slice(-3, None, None))] = 5
+        setitem_68 = attn_mask_35
+        setitem_68 = None
+        attn_mask_35[(slice(-3, None, None), slice(0, -7, None))] = 6
+        setitem_69 = attn_mask_35
+        setitem_69 = None
+        attn_mask_35[(slice(-3, None, None), slice(-7, -3, None))] = 7
+        setitem_70 = attn_mask_35
+        setitem_70 = None
+        attn_mask_35[(slice(-3, None, None), slice(-3, None, None))] = 8
+        setitem_71 = attn_mask_35
+        setitem_71 = None
+        attn_mask_36 = attn_mask_35.view(2, 7, 2, 7)
+        attn_mask_35 = None
+        permute_71 = attn_mask_36.permute(0, 2, 1, 3)
+        attn_mask_36 = None
+        attn_mask_37 = permute_71.reshape(4, 49)
+        permute_71 = None
+        unsqueeze_44 = attn_mask_37.unsqueeze(1)
+        unsqueeze_45 = attn_mask_37.unsqueeze(2)
+        attn_mask_37 = None
+        attn_mask_38 = unsqueeze_44 - unsqueeze_45
+        unsqueeze_44 = unsqueeze_45 = None
+        ne_7 = attn_mask_38 != 0
+        masked_fill_14 = attn_mask_38.masked_fill(ne_7, -100.0)
+        ne_7 = None
+        eq_7 = attn_mask_38 == 0
+        attn_mask_38 = None
+        attn_mask_39 = masked_fill_14.masked_fill(eq_7, 0.0)
+        masked_fill_14 = eq_7 = None
+        attn_83 = attn_82.view(1, 4, 16, 49, 49)
+        attn_82 = None
+        unsqueeze_46 = attn_mask_39.unsqueeze(1)
+        attn_mask_39 = None
+        unsqueeze_47 = unsqueeze_46.unsqueeze(0)
+        unsqueeze_46 = None
+        attn_84 = attn_83 + unsqueeze_47
+        attn_83 = unsqueeze_47 = None
+        attn_85 = attn_84.view(-1, 16, 49, 49)
+        attn_84 = None
+        attn_86 = torch.nn.functional.softmax(attn_85, dim=-1)
+        attn_85 = None
+        attn_87 = torch.nn.functional.dropout(attn_86, p=0.0, training=False)
+        attn_86 = None
+        matmul_31 = attn_87.matmul(v_15)
+        attn_87 = v_15 = None
+        transpose_31 = matmul_31.transpose(1, 2)
+        matmul_31 = None
+        x_191 = transpose_31.reshape(4, 49, 512)
+        transpose_31 = None
+        x_192 = torch._C._nn.linear(
+            x_191,
+            l_self_modules_features_modules_5_modules_11_modules_attn_modules_proj_parameters_weight_,
+            l_self_modules_features_modules_5_modules_11_modules_attn_modules_proj_parameters_bias_,
+        )
+        x_191 = l_self_modules_features_modules_5_modules_11_modules_attn_modules_proj_parameters_weight_ = l_self_modules_features_modules_5_modules_11_modules_attn_modules_proj_parameters_bias_ = (None)
+        x_193 = torch.nn.functional.dropout(x_192, p=0.0, training=False)
+        x_192 = None
+        x_194 = x_193.view(1, 2, 2, 7, 7, 512)
+        x_193 = None
+        permute_72 = x_194.permute(0, 1, 3, 2, 4, 5)
+        x_194 = None
+        x_195 = permute_72.reshape(1, 14, 14, 512)
+        permute_72 = None
+        x_196 = torch.roll(x_195, shifts=(3, 3), dims=(1, 2))
+        x_195 = None
+        getitem_87 = x_196[
+            (
+                slice(None, None, None),
+                slice(None, 14, None),
+                slice(None, 14, None),
+                slice(None, None, None),
+            )
+        ]
+        x_196 = None
+        x_197 = getitem_87.contiguous()
+        getitem_87 = None
+        _log_api_usage_once_30 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_30 = None
+        x_198 = x_186 + x_197
+        x_186 = x_197 = None
+        layer_norm_34 = torch.nn.functional.layer_norm(
+            x_198,
+            (512,),
+            l_self_modules_features_modules_5_modules_11_modules_norm2_parameters_weight_,
+            l_self_modules_features_modules_5_modules_11_modules_norm2_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_5_modules_11_modules_norm2_parameters_weight_ = (
+            l_self_modules_features_modules_5_modules_11_modules_norm2_parameters_bias_
+        ) = None
+        input_79 = torch._C._nn.linear(
+            layer_norm_34,
+            l_self_modules_features_modules_5_modules_11_modules_mlp_modules_0_parameters_weight_,
+            l_self_modules_features_modules_5_modules_11_modules_mlp_modules_0_parameters_bias_,
+        )
+        layer_norm_34 = l_self_modules_features_modules_5_modules_11_modules_mlp_modules_0_parameters_weight_ = l_self_modules_features_modules_5_modules_11_modules_mlp_modules_0_parameters_bias_ = (None)
+        input_80 = torch._C._nn.gelu(input_79, approximate="none")
+        input_79 = None
+        input_81 = torch.nn.functional.dropout(input_80, 0.0, False, False)
+        input_80 = None
+        input_82 = torch._C._nn.linear(
+            input_81,
+            l_self_modules_features_modules_5_modules_11_modules_mlp_modules_3_parameters_weight_,
+            l_self_modules_features_modules_5_modules_11_modules_mlp_modules_3_parameters_bias_,
+        )
+        input_81 = l_self_modules_features_modules_5_modules_11_modules_mlp_modules_3_parameters_weight_ = l_self_modules_features_modules_5_modules_11_modules_mlp_modules_3_parameters_bias_ = (None)
+        input_83 = torch.nn.functional.dropout(input_82, 0.0, False, False)
+        input_82 = None
+        _log_api_usage_once_31 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_31 = None
+        x_199 = x_198 + input_83
+        x_198 = input_83 = None
+        layer_norm_35 = torch.nn.functional.layer_norm(
+            x_199,
+            (512,),
+            l_self_modules_features_modules_5_modules_12_modules_norm1_parameters_weight_,
+            l_self_modules_features_modules_5_modules_12_modules_norm1_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_5_modules_12_modules_norm1_parameters_weight_ = (
+            l_self_modules_features_modules_5_modules_12_modules_norm1_parameters_bias_
+        ) = None
+        relative_position_bias_48 = l_self_modules_features_modules_5_modules_12_modules_attn_parameters_relative_position_bias_table_[
+            l_self_modules_features_modules_5_modules_12_modules_attn_buffers_relative_position_index_
+        ]
+        l_self_modules_features_modules_5_modules_12_modules_attn_parameters_relative_position_bias_table_ = l_self_modules_features_modules_5_modules_12_modules_attn_buffers_relative_position_index_ = (None)
+        relative_position_bias_49 = relative_position_bias_48.view(49, 49, -1)
+        relative_position_bias_48 = None
+        permute_73 = relative_position_bias_49.permute(2, 0, 1)
+        relative_position_bias_49 = None
+        contiguous_32 = permute_73.contiguous()
+        permute_73 = None
+        relative_position_bias_50 = contiguous_32.unsqueeze(0)
+        contiguous_32 = None
+        x_200 = torch._C._nn.pad(layer_norm_35, (0, 0, 0, 0, 0, 0), "constant", None)
+        layer_norm_35 = None
+        x_201 = x_200.view(1, 2, 7, 2, 7, 512)
+        x_200 = None
+        permute_74 = x_201.permute(0, 1, 3, 2, 4, 5)
+        x_201 = None
+        x_202 = permute_74.reshape(4, 49, 512)
+        permute_74 = None
+        qkv_32 = torch._C._nn.linear(
+            x_202,
+            l_self_modules_features_modules_5_modules_12_modules_attn_modules_qkv_parameters_weight_,
+            l_self_modules_features_modules_5_modules_12_modules_attn_modules_qkv_parameters_bias_,
+        )
+        x_202 = l_self_modules_features_modules_5_modules_12_modules_attn_modules_qkv_parameters_weight_ = l_self_modules_features_modules_5_modules_12_modules_attn_modules_qkv_parameters_bias_ = (None)
+        reshape_73 = qkv_32.reshape(4, 49, 3, 16, 32)
+        qkv_32 = None
+        qkv_33 = reshape_73.permute(2, 0, 3, 1, 4)
+        reshape_73 = None
+        q_32 = qkv_33[0]
+        k_16 = qkv_33[1]
+        v_16 = qkv_33[2]
+        qkv_33 = None
+        q_33 = q_32 * 0.1767766952966369
+        q_32 = None
+        transpose_32 = k_16.transpose(-2, -1)
+        k_16 = None
+        attn_88 = q_33.matmul(transpose_32)
+        q_33 = transpose_32 = None
+        attn_89 = attn_88 + relative_position_bias_50
+        attn_88 = relative_position_bias_50 = None
+        attn_90 = torch.nn.functional.softmax(attn_89, dim=-1)
+        attn_89 = None
+        attn_91 = torch.nn.functional.dropout(attn_90, p=0.0, training=False)
+        attn_90 = None
+        matmul_33 = attn_91.matmul(v_16)
+        attn_91 = v_16 = None
+        transpose_33 = matmul_33.transpose(1, 2)
+        matmul_33 = None
+        x_203 = transpose_33.reshape(4, 49, 512)
+        transpose_33 = None
+        x_204 = torch._C._nn.linear(
+            x_203,
+            l_self_modules_features_modules_5_modules_12_modules_attn_modules_proj_parameters_weight_,
+            l_self_modules_features_modules_5_modules_12_modules_attn_modules_proj_parameters_bias_,
+        )
+        x_203 = l_self_modules_features_modules_5_modules_12_modules_attn_modules_proj_parameters_weight_ = l_self_modules_features_modules_5_modules_12_modules_attn_modules_proj_parameters_bias_ = (None)
+        x_205 = torch.nn.functional.dropout(x_204, p=0.0, training=False)
+        x_204 = None
+        x_206 = x_205.view(1, 2, 2, 7, 7, 512)
+        x_205 = None
+        permute_76 = x_206.permute(0, 1, 3, 2, 4, 5)
+        x_206 = None
+        x_207 = permute_76.reshape(1, 14, 14, 512)
+        permute_76 = None
+        getitem_92 = x_207[
+            (
+                slice(None, None, None),
+                slice(None, 14, None),
+                slice(None, 14, None),
+                slice(None, None, None),
+            )
+        ]
+        x_207 = None
+        x_208 = getitem_92.contiguous()
+        getitem_92 = None
+        _log_api_usage_once_32 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_32 = None
+        x_209 = x_199 + x_208
+        x_199 = x_208 = None
+        layer_norm_36 = torch.nn.functional.layer_norm(
+            x_209,
+            (512,),
+            l_self_modules_features_modules_5_modules_12_modules_norm2_parameters_weight_,
+            l_self_modules_features_modules_5_modules_12_modules_norm2_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_5_modules_12_modules_norm2_parameters_weight_ = (
+            l_self_modules_features_modules_5_modules_12_modules_norm2_parameters_bias_
+        ) = None
+        input_84 = torch._C._nn.linear(
+            layer_norm_36,
+            l_self_modules_features_modules_5_modules_12_modules_mlp_modules_0_parameters_weight_,
+            l_self_modules_features_modules_5_modules_12_modules_mlp_modules_0_parameters_bias_,
+        )
+        layer_norm_36 = l_self_modules_features_modules_5_modules_12_modules_mlp_modules_0_parameters_weight_ = l_self_modules_features_modules_5_modules_12_modules_mlp_modules_0_parameters_bias_ = (None)
+        input_85 = torch._C._nn.gelu(input_84, approximate="none")
+        input_84 = None
+        input_86 = torch.nn.functional.dropout(input_85, 0.0, False, False)
+        input_85 = None
+        input_87 = torch._C._nn.linear(
+            input_86,
+            l_self_modules_features_modules_5_modules_12_modules_mlp_modules_3_parameters_weight_,
+            l_self_modules_features_modules_5_modules_12_modules_mlp_modules_3_parameters_bias_,
+        )
+        input_86 = l_self_modules_features_modules_5_modules_12_modules_mlp_modules_3_parameters_weight_ = l_self_modules_features_modules_5_modules_12_modules_mlp_modules_3_parameters_bias_ = (None)
+        input_88 = torch.nn.functional.dropout(input_87, 0.0, False, False)
+        input_87 = None
+        _log_api_usage_once_33 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_33 = None
+        x_210 = x_209 + input_88
+        x_209 = input_88 = None
+        layer_norm_37 = torch.nn.functional.layer_norm(
+            x_210,
+            (512,),
+            l_self_modules_features_modules_5_modules_13_modules_norm1_parameters_weight_,
+            l_self_modules_features_modules_5_modules_13_modules_norm1_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_5_modules_13_modules_norm1_parameters_weight_ = (
+            l_self_modules_features_modules_5_modules_13_modules_norm1_parameters_bias_
+        ) = None
+        relative_position_bias_51 = l_self_modules_features_modules_5_modules_13_modules_attn_parameters_relative_position_bias_table_[
+            l_self_modules_features_modules_5_modules_13_modules_attn_buffers_relative_position_index_
+        ]
+        l_self_modules_features_modules_5_modules_13_modules_attn_parameters_relative_position_bias_table_ = l_self_modules_features_modules_5_modules_13_modules_attn_buffers_relative_position_index_ = (None)
+        relative_position_bias_52 = relative_position_bias_51.view(49, 49, -1)
+        relative_position_bias_51 = None
+        permute_77 = relative_position_bias_52.permute(2, 0, 1)
+        relative_position_bias_52 = None
+        contiguous_34 = permute_77.contiguous()
+        permute_77 = None
+        relative_position_bias_53 = contiguous_34.unsqueeze(0)
+        contiguous_34 = None
+        x_211 = torch._C._nn.pad(layer_norm_37, (0, 0, 0, 0, 0, 0), "constant", None)
+        layer_norm_37 = None
+        x_212 = torch.roll(x_211, shifts=(-3, -3), dims=(1, 2))
+        x_211 = None
+        x_213 = x_212.view(1, 2, 7, 2, 7, 512)
+        x_212 = None
+        permute_78 = x_213.permute(0, 1, 3, 2, 4, 5)
+        x_213 = None
+        x_214 = permute_78.reshape(4, 49, 512)
+        permute_78 = None
+        qkv_34 = torch._C._nn.linear(
+            x_214,
+            l_self_modules_features_modules_5_modules_13_modules_attn_modules_qkv_parameters_weight_,
+            l_self_modules_features_modules_5_modules_13_modules_attn_modules_qkv_parameters_bias_,
+        )
+        l_self_modules_features_modules_5_modules_13_modules_attn_modules_qkv_parameters_weight_ = l_self_modules_features_modules_5_modules_13_modules_attn_modules_qkv_parameters_bias_ = (None)
+        reshape_77 = qkv_34.reshape(4, 49, 3, 16, 32)
+        qkv_34 = None
+        qkv_35 = reshape_77.permute(2, 0, 3, 1, 4)
+        reshape_77 = None
+        q_34 = qkv_35[0]
+        k_17 = qkv_35[1]
+        v_17 = qkv_35[2]
+        qkv_35 = None
+        q_35 = q_34 * 0.1767766952966369
+        q_34 = None
+        transpose_34 = k_17.transpose(-2, -1)
+        k_17 = None
+        attn_92 = q_35.matmul(transpose_34)
+        q_35 = transpose_34 = None
+        attn_93 = attn_92 + relative_position_bias_53
+        attn_92 = relative_position_bias_53 = None
+        attn_mask_40 = x_214.new_zeros((14, 14))
+        x_214 = None
+        attn_mask_40[(slice(0, -7, None), slice(0, -7, None))] = 0
+        setitem_72 = attn_mask_40
+        setitem_72 = None
+        attn_mask_40[(slice(0, -7, None), slice(-7, -3, None))] = 1
+        setitem_73 = attn_mask_40
+        setitem_73 = None
+        attn_mask_40[(slice(0, -7, None), slice(-3, None, None))] = 2
+        setitem_74 = attn_mask_40
+        setitem_74 = None
+        attn_mask_40[(slice(-7, -3, None), slice(0, -7, None))] = 3
+        setitem_75 = attn_mask_40
+        setitem_75 = None
+        attn_mask_40[(slice(-7, -3, None), slice(-7, -3, None))] = 4
+        setitem_76 = attn_mask_40
+        setitem_76 = None
+        attn_mask_40[(slice(-7, -3, None), slice(-3, None, None))] = 5
+        setitem_77 = attn_mask_40
+        setitem_77 = None
+        attn_mask_40[(slice(-3, None, None), slice(0, -7, None))] = 6
+        setitem_78 = attn_mask_40
+        setitem_78 = None
+        attn_mask_40[(slice(-3, None, None), slice(-7, -3, None))] = 7
+        setitem_79 = attn_mask_40
+        setitem_79 = None
+        attn_mask_40[(slice(-3, None, None), slice(-3, None, None))] = 8
+        setitem_80 = attn_mask_40
+        setitem_80 = None
+        attn_mask_41 = attn_mask_40.view(2, 7, 2, 7)
+        attn_mask_40 = None
+        permute_80 = attn_mask_41.permute(0, 2, 1, 3)
+        attn_mask_41 = None
+        attn_mask_42 = permute_80.reshape(4, 49)
+        permute_80 = None
+        unsqueeze_50 = attn_mask_42.unsqueeze(1)
+        unsqueeze_51 = attn_mask_42.unsqueeze(2)
+        attn_mask_42 = None
+        attn_mask_43 = unsqueeze_50 - unsqueeze_51
+        unsqueeze_50 = unsqueeze_51 = None
+        ne_8 = attn_mask_43 != 0
+        masked_fill_16 = attn_mask_43.masked_fill(ne_8, -100.0)
+        ne_8 = None
+        eq_8 = attn_mask_43 == 0
+        attn_mask_43 = None
+        attn_mask_44 = masked_fill_16.masked_fill(eq_8, 0.0)
+        masked_fill_16 = eq_8 = None
+        attn_94 = attn_93.view(1, 4, 16, 49, 49)
+        attn_93 = None
+        unsqueeze_52 = attn_mask_44.unsqueeze(1)
+        attn_mask_44 = None
+        unsqueeze_53 = unsqueeze_52.unsqueeze(0)
+        unsqueeze_52 = None
+        attn_95 = attn_94 + unsqueeze_53
+        attn_94 = unsqueeze_53 = None
+        attn_96 = attn_95.view(-1, 16, 49, 49)
+        attn_95 = None
+        attn_97 = torch.nn.functional.softmax(attn_96, dim=-1)
+        attn_96 = None
+        attn_98 = torch.nn.functional.dropout(attn_97, p=0.0, training=False)
+        attn_97 = None
+        matmul_35 = attn_98.matmul(v_17)
+        attn_98 = v_17 = None
+        transpose_35 = matmul_35.transpose(1, 2)
+        matmul_35 = None
+        x_215 = transpose_35.reshape(4, 49, 512)
+        transpose_35 = None
+        x_216 = torch._C._nn.linear(
+            x_215,
+            l_self_modules_features_modules_5_modules_13_modules_attn_modules_proj_parameters_weight_,
+            l_self_modules_features_modules_5_modules_13_modules_attn_modules_proj_parameters_bias_,
+        )
+        x_215 = l_self_modules_features_modules_5_modules_13_modules_attn_modules_proj_parameters_weight_ = l_self_modules_features_modules_5_modules_13_modules_attn_modules_proj_parameters_bias_ = (None)
+        x_217 = torch.nn.functional.dropout(x_216, p=0.0, training=False)
+        x_216 = None
+        x_218 = x_217.view(1, 2, 2, 7, 7, 512)
+        x_217 = None
+        permute_81 = x_218.permute(0, 1, 3, 2, 4, 5)
+        x_218 = None
+        x_219 = permute_81.reshape(1, 14, 14, 512)
+        permute_81 = None
+        x_220 = torch.roll(x_219, shifts=(3, 3), dims=(1, 2))
+        x_219 = None
+        getitem_97 = x_220[
+            (
+                slice(None, None, None),
+                slice(None, 14, None),
+                slice(None, 14, None),
+                slice(None, None, None),
+            )
+        ]
+        x_220 = None
+        x_221 = getitem_97.contiguous()
+        getitem_97 = None
+        _log_api_usage_once_34 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_34 = None
+        x_222 = x_210 + x_221
+        x_210 = x_221 = None
+        layer_norm_38 = torch.nn.functional.layer_norm(
+            x_222,
+            (512,),
+            l_self_modules_features_modules_5_modules_13_modules_norm2_parameters_weight_,
+            l_self_modules_features_modules_5_modules_13_modules_norm2_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_5_modules_13_modules_norm2_parameters_weight_ = (
+            l_self_modules_features_modules_5_modules_13_modules_norm2_parameters_bias_
+        ) = None
+        input_89 = torch._C._nn.linear(
+            layer_norm_38,
+            l_self_modules_features_modules_5_modules_13_modules_mlp_modules_0_parameters_weight_,
+            l_self_modules_features_modules_5_modules_13_modules_mlp_modules_0_parameters_bias_,
+        )
+        layer_norm_38 = l_self_modules_features_modules_5_modules_13_modules_mlp_modules_0_parameters_weight_ = l_self_modules_features_modules_5_modules_13_modules_mlp_modules_0_parameters_bias_ = (None)
+        input_90 = torch._C._nn.gelu(input_89, approximate="none")
+        input_89 = None
+        input_91 = torch.nn.functional.dropout(input_90, 0.0, False, False)
+        input_90 = None
+        input_92 = torch._C._nn.linear(
+            input_91,
+            l_self_modules_features_modules_5_modules_13_modules_mlp_modules_3_parameters_weight_,
+            l_self_modules_features_modules_5_modules_13_modules_mlp_modules_3_parameters_bias_,
+        )
+        input_91 = l_self_modules_features_modules_5_modules_13_modules_mlp_modules_3_parameters_weight_ = l_self_modules_features_modules_5_modules_13_modules_mlp_modules_3_parameters_bias_ = (None)
+        input_93 = torch.nn.functional.dropout(input_92, 0.0, False, False)
+        input_92 = None
+        _log_api_usage_once_35 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_35 = None
+        x_223 = x_222 + input_93
+        x_222 = input_93 = None
+        layer_norm_39 = torch.nn.functional.layer_norm(
+            x_223,
+            (512,),
+            l_self_modules_features_modules_5_modules_14_modules_norm1_parameters_weight_,
+            l_self_modules_features_modules_5_modules_14_modules_norm1_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_5_modules_14_modules_norm1_parameters_weight_ = (
+            l_self_modules_features_modules_5_modules_14_modules_norm1_parameters_bias_
+        ) = None
+        relative_position_bias_54 = l_self_modules_features_modules_5_modules_14_modules_attn_parameters_relative_position_bias_table_[
+            l_self_modules_features_modules_5_modules_14_modules_attn_buffers_relative_position_index_
+        ]
+        l_self_modules_features_modules_5_modules_14_modules_attn_parameters_relative_position_bias_table_ = l_self_modules_features_modules_5_modules_14_modules_attn_buffers_relative_position_index_ = (None)
+        relative_position_bias_55 = relative_position_bias_54.view(49, 49, -1)
+        relative_position_bias_54 = None
+        permute_82 = relative_position_bias_55.permute(2, 0, 1)
+        relative_position_bias_55 = None
+        contiguous_36 = permute_82.contiguous()
+        permute_82 = None
+        relative_position_bias_56 = contiguous_36.unsqueeze(0)
+        contiguous_36 = None
+        x_224 = torch._C._nn.pad(layer_norm_39, (0, 0, 0, 0, 0, 0), "constant", None)
+        layer_norm_39 = None
+        x_225 = x_224.view(1, 2, 7, 2, 7, 512)
+        x_224 = None
+        permute_83 = x_225.permute(0, 1, 3, 2, 4, 5)
+        x_225 = None
+        x_226 = permute_83.reshape(4, 49, 512)
+        permute_83 = None
+        qkv_36 = torch._C._nn.linear(
+            x_226,
+            l_self_modules_features_modules_5_modules_14_modules_attn_modules_qkv_parameters_weight_,
+            l_self_modules_features_modules_5_modules_14_modules_attn_modules_qkv_parameters_bias_,
+        )
+        x_226 = l_self_modules_features_modules_5_modules_14_modules_attn_modules_qkv_parameters_weight_ = l_self_modules_features_modules_5_modules_14_modules_attn_modules_qkv_parameters_bias_ = (None)
+        reshape_82 = qkv_36.reshape(4, 49, 3, 16, 32)
+        qkv_36 = None
+        qkv_37 = reshape_82.permute(2, 0, 3, 1, 4)
+        reshape_82 = None
+        q_36 = qkv_37[0]
+        k_18 = qkv_37[1]
+        v_18 = qkv_37[2]
+        qkv_37 = None
+        q_37 = q_36 * 0.1767766952966369
+        q_36 = None
+        transpose_36 = k_18.transpose(-2, -1)
+        k_18 = None
+        attn_99 = q_37.matmul(transpose_36)
+        q_37 = transpose_36 = None
+        attn_100 = attn_99 + relative_position_bias_56
+        attn_99 = relative_position_bias_56 = None
+        attn_101 = torch.nn.functional.softmax(attn_100, dim=-1)
+        attn_100 = None
+        attn_102 = torch.nn.functional.dropout(attn_101, p=0.0, training=False)
+        attn_101 = None
+        matmul_37 = attn_102.matmul(v_18)
+        attn_102 = v_18 = None
+        transpose_37 = matmul_37.transpose(1, 2)
+        matmul_37 = None
+        x_227 = transpose_37.reshape(4, 49, 512)
+        transpose_37 = None
+        x_228 = torch._C._nn.linear(
+            x_227,
+            l_self_modules_features_modules_5_modules_14_modules_attn_modules_proj_parameters_weight_,
+            l_self_modules_features_modules_5_modules_14_modules_attn_modules_proj_parameters_bias_,
+        )
+        x_227 = l_self_modules_features_modules_5_modules_14_modules_attn_modules_proj_parameters_weight_ = l_self_modules_features_modules_5_modules_14_modules_attn_modules_proj_parameters_bias_ = (None)
+        x_229 = torch.nn.functional.dropout(x_228, p=0.0, training=False)
+        x_228 = None
+        x_230 = x_229.view(1, 2, 2, 7, 7, 512)
+        x_229 = None
+        permute_85 = x_230.permute(0, 1, 3, 2, 4, 5)
+        x_230 = None
+        x_231 = permute_85.reshape(1, 14, 14, 512)
+        permute_85 = None
+        getitem_102 = x_231[
+            (
+                slice(None, None, None),
+                slice(None, 14, None),
+                slice(None, 14, None),
+                slice(None, None, None),
+            )
+        ]
+        x_231 = None
+        x_232 = getitem_102.contiguous()
+        getitem_102 = None
+        _log_api_usage_once_36 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_36 = None
+        x_233 = x_223 + x_232
+        x_223 = x_232 = None
+        layer_norm_40 = torch.nn.functional.layer_norm(
+            x_233,
+            (512,),
+            l_self_modules_features_modules_5_modules_14_modules_norm2_parameters_weight_,
+            l_self_modules_features_modules_5_modules_14_modules_norm2_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_5_modules_14_modules_norm2_parameters_weight_ = (
+            l_self_modules_features_modules_5_modules_14_modules_norm2_parameters_bias_
+        ) = None
+        input_94 = torch._C._nn.linear(
+            layer_norm_40,
+            l_self_modules_features_modules_5_modules_14_modules_mlp_modules_0_parameters_weight_,
+            l_self_modules_features_modules_5_modules_14_modules_mlp_modules_0_parameters_bias_,
+        )
+        layer_norm_40 = l_self_modules_features_modules_5_modules_14_modules_mlp_modules_0_parameters_weight_ = l_self_modules_features_modules_5_modules_14_modules_mlp_modules_0_parameters_bias_ = (None)
+        input_95 = torch._C._nn.gelu(input_94, approximate="none")
+        input_94 = None
+        input_96 = torch.nn.functional.dropout(input_95, 0.0, False, False)
+        input_95 = None
+        input_97 = torch._C._nn.linear(
+            input_96,
+            l_self_modules_features_modules_5_modules_14_modules_mlp_modules_3_parameters_weight_,
+            l_self_modules_features_modules_5_modules_14_modules_mlp_modules_3_parameters_bias_,
+        )
+        input_96 = l_self_modules_features_modules_5_modules_14_modules_mlp_modules_3_parameters_weight_ = l_self_modules_features_modules_5_modules_14_modules_mlp_modules_3_parameters_bias_ = (None)
+        input_98 = torch.nn.functional.dropout(input_97, 0.0, False, False)
+        input_97 = None
+        _log_api_usage_once_37 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_37 = None
+        x_234 = x_233 + input_98
+        x_233 = input_98 = None
+        layer_norm_41 = torch.nn.functional.layer_norm(
+            x_234,
+            (512,),
+            l_self_modules_features_modules_5_modules_15_modules_norm1_parameters_weight_,
+            l_self_modules_features_modules_5_modules_15_modules_norm1_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_5_modules_15_modules_norm1_parameters_weight_ = (
+            l_self_modules_features_modules_5_modules_15_modules_norm1_parameters_bias_
+        ) = None
+        relative_position_bias_57 = l_self_modules_features_modules_5_modules_15_modules_attn_parameters_relative_position_bias_table_[
+            l_self_modules_features_modules_5_modules_15_modules_attn_buffers_relative_position_index_
+        ]
+        l_self_modules_features_modules_5_modules_15_modules_attn_parameters_relative_position_bias_table_ = l_self_modules_features_modules_5_modules_15_modules_attn_buffers_relative_position_index_ = (None)
+        relative_position_bias_58 = relative_position_bias_57.view(49, 49, -1)
+        relative_position_bias_57 = None
+        permute_86 = relative_position_bias_58.permute(2, 0, 1)
+        relative_position_bias_58 = None
+        contiguous_38 = permute_86.contiguous()
+        permute_86 = None
+        relative_position_bias_59 = contiguous_38.unsqueeze(0)
+        contiguous_38 = None
+        x_235 = torch._C._nn.pad(layer_norm_41, (0, 0, 0, 0, 0, 0), "constant", None)
+        layer_norm_41 = None
+        x_236 = torch.roll(x_235, shifts=(-3, -3), dims=(1, 2))
+        x_235 = None
+        x_237 = x_236.view(1, 2, 7, 2, 7, 512)
+        x_236 = None
+        permute_87 = x_237.permute(0, 1, 3, 2, 4, 5)
+        x_237 = None
+        x_238 = permute_87.reshape(4, 49, 512)
+        permute_87 = None
+        qkv_38 = torch._C._nn.linear(
+            x_238,
+            l_self_modules_features_modules_5_modules_15_modules_attn_modules_qkv_parameters_weight_,
+            l_self_modules_features_modules_5_modules_15_modules_attn_modules_qkv_parameters_bias_,
+        )
+        l_self_modules_features_modules_5_modules_15_modules_attn_modules_qkv_parameters_weight_ = l_self_modules_features_modules_5_modules_15_modules_attn_modules_qkv_parameters_bias_ = (None)
+        reshape_86 = qkv_38.reshape(4, 49, 3, 16, 32)
+        qkv_38 = None
+        qkv_39 = reshape_86.permute(2, 0, 3, 1, 4)
+        reshape_86 = None
+        q_38 = qkv_39[0]
+        k_19 = qkv_39[1]
+        v_19 = qkv_39[2]
+        qkv_39 = None
+        q_39 = q_38 * 0.1767766952966369
+        q_38 = None
+        transpose_38 = k_19.transpose(-2, -1)
+        k_19 = None
+        attn_103 = q_39.matmul(transpose_38)
+        q_39 = transpose_38 = None
+        attn_104 = attn_103 + relative_position_bias_59
+        attn_103 = relative_position_bias_59 = None
+        attn_mask_45 = x_238.new_zeros((14, 14))
+        x_238 = None
+        attn_mask_45[(slice(0, -7, None), slice(0, -7, None))] = 0
+        setitem_81 = attn_mask_45
+        setitem_81 = None
+        attn_mask_45[(slice(0, -7, None), slice(-7, -3, None))] = 1
+        setitem_82 = attn_mask_45
+        setitem_82 = None
+        attn_mask_45[(slice(0, -7, None), slice(-3, None, None))] = 2
+        setitem_83 = attn_mask_45
+        setitem_83 = None
+        attn_mask_45[(slice(-7, -3, None), slice(0, -7, None))] = 3
+        setitem_84 = attn_mask_45
+        setitem_84 = None
+        attn_mask_45[(slice(-7, -3, None), slice(-7, -3, None))] = 4
+        setitem_85 = attn_mask_45
+        setitem_85 = None
+        attn_mask_45[(slice(-7, -3, None), slice(-3, None, None))] = 5
+        setitem_86 = attn_mask_45
+        setitem_86 = None
+        attn_mask_45[(slice(-3, None, None), slice(0, -7, None))] = 6
+        setitem_87 = attn_mask_45
+        setitem_87 = None
+        attn_mask_45[(slice(-3, None, None), slice(-7, -3, None))] = 7
+        setitem_88 = attn_mask_45
+        setitem_88 = None
+        attn_mask_45[(slice(-3, None, None), slice(-3, None, None))] = 8
+        setitem_89 = attn_mask_45
+        setitem_89 = None
+        attn_mask_46 = attn_mask_45.view(2, 7, 2, 7)
+        attn_mask_45 = None
+        permute_89 = attn_mask_46.permute(0, 2, 1, 3)
+        attn_mask_46 = None
+        attn_mask_47 = permute_89.reshape(4, 49)
+        permute_89 = None
+        unsqueeze_56 = attn_mask_47.unsqueeze(1)
+        unsqueeze_57 = attn_mask_47.unsqueeze(2)
+        attn_mask_47 = None
+        attn_mask_48 = unsqueeze_56 - unsqueeze_57
+        unsqueeze_56 = unsqueeze_57 = None
+        ne_9 = attn_mask_48 != 0
+        masked_fill_18 = attn_mask_48.masked_fill(ne_9, -100.0)
+        ne_9 = None
+        eq_9 = attn_mask_48 == 0
+        attn_mask_48 = None
+        attn_mask_49 = masked_fill_18.masked_fill(eq_9, 0.0)
+        masked_fill_18 = eq_9 = None
+        attn_105 = attn_104.view(1, 4, 16, 49, 49)
+        attn_104 = None
+        unsqueeze_58 = attn_mask_49.unsqueeze(1)
+        attn_mask_49 = None
+        unsqueeze_59 = unsqueeze_58.unsqueeze(0)
+        unsqueeze_58 = None
+        attn_106 = attn_105 + unsqueeze_59
+        attn_105 = unsqueeze_59 = None
+        attn_107 = attn_106.view(-1, 16, 49, 49)
+        attn_106 = None
+        attn_108 = torch.nn.functional.softmax(attn_107, dim=-1)
+        attn_107 = None
+        attn_109 = torch.nn.functional.dropout(attn_108, p=0.0, training=False)
+        attn_108 = None
+        matmul_39 = attn_109.matmul(v_19)
+        attn_109 = v_19 = None
+        transpose_39 = matmul_39.transpose(1, 2)
+        matmul_39 = None
+        x_239 = transpose_39.reshape(4, 49, 512)
+        transpose_39 = None
+        x_240 = torch._C._nn.linear(
+            x_239,
+            l_self_modules_features_modules_5_modules_15_modules_attn_modules_proj_parameters_weight_,
+            l_self_modules_features_modules_5_modules_15_modules_attn_modules_proj_parameters_bias_,
+        )
+        x_239 = l_self_modules_features_modules_5_modules_15_modules_attn_modules_proj_parameters_weight_ = l_self_modules_features_modules_5_modules_15_modules_attn_modules_proj_parameters_bias_ = (None)
+        x_241 = torch.nn.functional.dropout(x_240, p=0.0, training=False)
+        x_240 = None
+        x_242 = x_241.view(1, 2, 2, 7, 7, 512)
+        x_241 = None
+        permute_90 = x_242.permute(0, 1, 3, 2, 4, 5)
+        x_242 = None
+        x_243 = permute_90.reshape(1, 14, 14, 512)
+        permute_90 = None
+        x_244 = torch.roll(x_243, shifts=(3, 3), dims=(1, 2))
+        x_243 = None
+        getitem_107 = x_244[
+            (
+                slice(None, None, None),
+                slice(None, 14, None),
+                slice(None, 14, None),
+                slice(None, None, None),
+            )
+        ]
+        x_244 = None
+        x_245 = getitem_107.contiguous()
+        getitem_107 = None
+        _log_api_usage_once_38 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_38 = None
+        x_246 = x_234 + x_245
+        x_234 = x_245 = None
+        layer_norm_42 = torch.nn.functional.layer_norm(
+            x_246,
+            (512,),
+            l_self_modules_features_modules_5_modules_15_modules_norm2_parameters_weight_,
+            l_self_modules_features_modules_5_modules_15_modules_norm2_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_5_modules_15_modules_norm2_parameters_weight_ = (
+            l_self_modules_features_modules_5_modules_15_modules_norm2_parameters_bias_
+        ) = None
+        input_99 = torch._C._nn.linear(
+            layer_norm_42,
+            l_self_modules_features_modules_5_modules_15_modules_mlp_modules_0_parameters_weight_,
+            l_self_modules_features_modules_5_modules_15_modules_mlp_modules_0_parameters_bias_,
+        )
+        layer_norm_42 = l_self_modules_features_modules_5_modules_15_modules_mlp_modules_0_parameters_weight_ = l_self_modules_features_modules_5_modules_15_modules_mlp_modules_0_parameters_bias_ = (None)
+        input_100 = torch._C._nn.gelu(input_99, approximate="none")
+        input_99 = None
+        input_101 = torch.nn.functional.dropout(input_100, 0.0, False, False)
+        input_100 = None
+        input_102 = torch._C._nn.linear(
+            input_101,
+            l_self_modules_features_modules_5_modules_15_modules_mlp_modules_3_parameters_weight_,
+            l_self_modules_features_modules_5_modules_15_modules_mlp_modules_3_parameters_bias_,
+        )
+        input_101 = l_self_modules_features_modules_5_modules_15_modules_mlp_modules_3_parameters_weight_ = l_self_modules_features_modules_5_modules_15_modules_mlp_modules_3_parameters_bias_ = (None)
+        input_103 = torch.nn.functional.dropout(input_102, 0.0, False, False)
+        input_102 = None
+        _log_api_usage_once_39 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_39 = None
+        x_247 = x_246 + input_103
+        x_246 = input_103 = None
+        layer_norm_43 = torch.nn.functional.layer_norm(
+            x_247,
+            (512,),
+            l_self_modules_features_modules_5_modules_16_modules_norm1_parameters_weight_,
+            l_self_modules_features_modules_5_modules_16_modules_norm1_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_5_modules_16_modules_norm1_parameters_weight_ = (
+            l_self_modules_features_modules_5_modules_16_modules_norm1_parameters_bias_
+        ) = None
+        relative_position_bias_60 = l_self_modules_features_modules_5_modules_16_modules_attn_parameters_relative_position_bias_table_[
+            l_self_modules_features_modules_5_modules_16_modules_attn_buffers_relative_position_index_
+        ]
+        l_self_modules_features_modules_5_modules_16_modules_attn_parameters_relative_position_bias_table_ = l_self_modules_features_modules_5_modules_16_modules_attn_buffers_relative_position_index_ = (None)
+        relative_position_bias_61 = relative_position_bias_60.view(49, 49, -1)
+        relative_position_bias_60 = None
+        permute_91 = relative_position_bias_61.permute(2, 0, 1)
+        relative_position_bias_61 = None
+        contiguous_40 = permute_91.contiguous()
+        permute_91 = None
+        relative_position_bias_62 = contiguous_40.unsqueeze(0)
+        contiguous_40 = None
+        x_248 = torch._C._nn.pad(layer_norm_43, (0, 0, 0, 0, 0, 0), "constant", None)
+        layer_norm_43 = None
+        x_249 = x_248.view(1, 2, 7, 2, 7, 512)
+        x_248 = None
+        permute_92 = x_249.permute(0, 1, 3, 2, 4, 5)
+        x_249 = None
+        x_250 = permute_92.reshape(4, 49, 512)
+        permute_92 = None
+        qkv_40 = torch._C._nn.linear(
+            x_250,
+            l_self_modules_features_modules_5_modules_16_modules_attn_modules_qkv_parameters_weight_,
+            l_self_modules_features_modules_5_modules_16_modules_attn_modules_qkv_parameters_bias_,
+        )
+        x_250 = l_self_modules_features_modules_5_modules_16_modules_attn_modules_qkv_parameters_weight_ = l_self_modules_features_modules_5_modules_16_modules_attn_modules_qkv_parameters_bias_ = (None)
+        reshape_91 = qkv_40.reshape(4, 49, 3, 16, 32)
+        qkv_40 = None
+        qkv_41 = reshape_91.permute(2, 0, 3, 1, 4)
+        reshape_91 = None
+        q_40 = qkv_41[0]
+        k_20 = qkv_41[1]
+        v_20 = qkv_41[2]
+        qkv_41 = None
+        q_41 = q_40 * 0.1767766952966369
+        q_40 = None
+        transpose_40 = k_20.transpose(-2, -1)
+        k_20 = None
+        attn_110 = q_41.matmul(transpose_40)
+        q_41 = transpose_40 = None
+        attn_111 = attn_110 + relative_position_bias_62
+        attn_110 = relative_position_bias_62 = None
+        attn_112 = torch.nn.functional.softmax(attn_111, dim=-1)
+        attn_111 = None
+        attn_113 = torch.nn.functional.dropout(attn_112, p=0.0, training=False)
+        attn_112 = None
+        matmul_41 = attn_113.matmul(v_20)
+        attn_113 = v_20 = None
+        transpose_41 = matmul_41.transpose(1, 2)
+        matmul_41 = None
+        x_251 = transpose_41.reshape(4, 49, 512)
+        transpose_41 = None
+        x_252 = torch._C._nn.linear(
+            x_251,
+            l_self_modules_features_modules_5_modules_16_modules_attn_modules_proj_parameters_weight_,
+            l_self_modules_features_modules_5_modules_16_modules_attn_modules_proj_parameters_bias_,
+        )
+        x_251 = l_self_modules_features_modules_5_modules_16_modules_attn_modules_proj_parameters_weight_ = l_self_modules_features_modules_5_modules_16_modules_attn_modules_proj_parameters_bias_ = (None)
+        x_253 = torch.nn.functional.dropout(x_252, p=0.0, training=False)
+        x_252 = None
+        x_254 = x_253.view(1, 2, 2, 7, 7, 512)
+        x_253 = None
+        permute_94 = x_254.permute(0, 1, 3, 2, 4, 5)
+        x_254 = None
+        x_255 = permute_94.reshape(1, 14, 14, 512)
+        permute_94 = None
+        getitem_112 = x_255[
+            (
+                slice(None, None, None),
+                slice(None, 14, None),
+                slice(None, 14, None),
+                slice(None, None, None),
+            )
+        ]
+        x_255 = None
+        x_256 = getitem_112.contiguous()
+        getitem_112 = None
+        _log_api_usage_once_40 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_40 = None
+        x_257 = x_247 + x_256
+        x_247 = x_256 = None
+        layer_norm_44 = torch.nn.functional.layer_norm(
+            x_257,
+            (512,),
+            l_self_modules_features_modules_5_modules_16_modules_norm2_parameters_weight_,
+            l_self_modules_features_modules_5_modules_16_modules_norm2_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_5_modules_16_modules_norm2_parameters_weight_ = (
+            l_self_modules_features_modules_5_modules_16_modules_norm2_parameters_bias_
+        ) = None
+        input_104 = torch._C._nn.linear(
+            layer_norm_44,
+            l_self_modules_features_modules_5_modules_16_modules_mlp_modules_0_parameters_weight_,
+            l_self_modules_features_modules_5_modules_16_modules_mlp_modules_0_parameters_bias_,
+        )
+        layer_norm_44 = l_self_modules_features_modules_5_modules_16_modules_mlp_modules_0_parameters_weight_ = l_self_modules_features_modules_5_modules_16_modules_mlp_modules_0_parameters_bias_ = (None)
+        input_105 = torch._C._nn.gelu(input_104, approximate="none")
+        input_104 = None
+        input_106 = torch.nn.functional.dropout(input_105, 0.0, False, False)
+        input_105 = None
+        input_107 = torch._C._nn.linear(
+            input_106,
+            l_self_modules_features_modules_5_modules_16_modules_mlp_modules_3_parameters_weight_,
+            l_self_modules_features_modules_5_modules_16_modules_mlp_modules_3_parameters_bias_,
+        )
+        input_106 = l_self_modules_features_modules_5_modules_16_modules_mlp_modules_3_parameters_weight_ = l_self_modules_features_modules_5_modules_16_modules_mlp_modules_3_parameters_bias_ = (None)
+        input_108 = torch.nn.functional.dropout(input_107, 0.0, False, False)
+        input_107 = None
+        _log_api_usage_once_41 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_41 = None
+        x_258 = x_257 + input_108
+        x_257 = input_108 = None
+        layer_norm_45 = torch.nn.functional.layer_norm(
+            x_258,
+            (512,),
+            l_self_modules_features_modules_5_modules_17_modules_norm1_parameters_weight_,
+            l_self_modules_features_modules_5_modules_17_modules_norm1_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_5_modules_17_modules_norm1_parameters_weight_ = (
+            l_self_modules_features_modules_5_modules_17_modules_norm1_parameters_bias_
+        ) = None
+        relative_position_bias_63 = l_self_modules_features_modules_5_modules_17_modules_attn_parameters_relative_position_bias_table_[
+            l_self_modules_features_modules_5_modules_17_modules_attn_buffers_relative_position_index_
+        ]
+        l_self_modules_features_modules_5_modules_17_modules_attn_parameters_relative_position_bias_table_ = l_self_modules_features_modules_5_modules_17_modules_attn_buffers_relative_position_index_ = (None)
+        relative_position_bias_64 = relative_position_bias_63.view(49, 49, -1)
+        relative_position_bias_63 = None
+        permute_95 = relative_position_bias_64.permute(2, 0, 1)
+        relative_position_bias_64 = None
+        contiguous_42 = permute_95.contiguous()
+        permute_95 = None
+        relative_position_bias_65 = contiguous_42.unsqueeze(0)
+        contiguous_42 = None
+        x_259 = torch._C._nn.pad(layer_norm_45, (0, 0, 0, 0, 0, 0), "constant", None)
+        layer_norm_45 = None
+        x_260 = torch.roll(x_259, shifts=(-3, -3), dims=(1, 2))
+        x_259 = None
+        x_261 = x_260.view(1, 2, 7, 2, 7, 512)
+        x_260 = None
+        permute_96 = x_261.permute(0, 1, 3, 2, 4, 5)
+        x_261 = None
+        x_262 = permute_96.reshape(4, 49, 512)
+        permute_96 = None
+        qkv_42 = torch._C._nn.linear(
+            x_262,
+            l_self_modules_features_modules_5_modules_17_modules_attn_modules_qkv_parameters_weight_,
+            l_self_modules_features_modules_5_modules_17_modules_attn_modules_qkv_parameters_bias_,
+        )
+        l_self_modules_features_modules_5_modules_17_modules_attn_modules_qkv_parameters_weight_ = l_self_modules_features_modules_5_modules_17_modules_attn_modules_qkv_parameters_bias_ = (None)
+        reshape_95 = qkv_42.reshape(4, 49, 3, 16, 32)
+        qkv_42 = None
+        qkv_43 = reshape_95.permute(2, 0, 3, 1, 4)
+        reshape_95 = None
+        q_42 = qkv_43[0]
+        k_21 = qkv_43[1]
+        v_21 = qkv_43[2]
+        qkv_43 = None
+        q_43 = q_42 * 0.1767766952966369
+        q_42 = None
+        transpose_42 = k_21.transpose(-2, -1)
+        k_21 = None
+        attn_114 = q_43.matmul(transpose_42)
+        q_43 = transpose_42 = None
+        attn_115 = attn_114 + relative_position_bias_65
+        attn_114 = relative_position_bias_65 = None
+        attn_mask_50 = x_262.new_zeros((14, 14))
+        x_262 = None
+        attn_mask_50[(slice(0, -7, None), slice(0, -7, None))] = 0
+        setitem_90 = attn_mask_50
+        setitem_90 = None
+        attn_mask_50[(slice(0, -7, None), slice(-7, -3, None))] = 1
+        setitem_91 = attn_mask_50
+        setitem_91 = None
+        attn_mask_50[(slice(0, -7, None), slice(-3, None, None))] = 2
+        setitem_92 = attn_mask_50
+        setitem_92 = None
+        attn_mask_50[(slice(-7, -3, None), slice(0, -7, None))] = 3
+        setitem_93 = attn_mask_50
+        setitem_93 = None
+        attn_mask_50[(slice(-7, -3, None), slice(-7, -3, None))] = 4
+        setitem_94 = attn_mask_50
+        setitem_94 = None
+        attn_mask_50[(slice(-7, -3, None), slice(-3, None, None))] = 5
+        setitem_95 = attn_mask_50
+        setitem_95 = None
+        attn_mask_50[(slice(-3, None, None), slice(0, -7, None))] = 6
+        setitem_96 = attn_mask_50
+        setitem_96 = None
+        attn_mask_50[(slice(-3, None, None), slice(-7, -3, None))] = 7
+        setitem_97 = attn_mask_50
+        setitem_97 = None
+        attn_mask_50[(slice(-3, None, None), slice(-3, None, None))] = 8
+        setitem_98 = attn_mask_50
+        setitem_98 = None
+        attn_mask_51 = attn_mask_50.view(2, 7, 2, 7)
+        attn_mask_50 = None
+        permute_98 = attn_mask_51.permute(0, 2, 1, 3)
+        attn_mask_51 = None
+        attn_mask_52 = permute_98.reshape(4, 49)
+        permute_98 = None
+        unsqueeze_62 = attn_mask_52.unsqueeze(1)
+        unsqueeze_63 = attn_mask_52.unsqueeze(2)
+        attn_mask_52 = None
+        attn_mask_53 = unsqueeze_62 - unsqueeze_63
+        unsqueeze_62 = unsqueeze_63 = None
+        ne_10 = attn_mask_53 != 0
+        masked_fill_20 = attn_mask_53.masked_fill(ne_10, -100.0)
+        ne_10 = None
+        eq_10 = attn_mask_53 == 0
+        attn_mask_53 = None
+        attn_mask_54 = masked_fill_20.masked_fill(eq_10, 0.0)
+        masked_fill_20 = eq_10 = None
+        attn_116 = attn_115.view(1, 4, 16, 49, 49)
+        attn_115 = None
+        unsqueeze_64 = attn_mask_54.unsqueeze(1)
+        attn_mask_54 = None
+        unsqueeze_65 = unsqueeze_64.unsqueeze(0)
+        unsqueeze_64 = None
+        attn_117 = attn_116 + unsqueeze_65
+        attn_116 = unsqueeze_65 = None
+        attn_118 = attn_117.view(-1, 16, 49, 49)
+        attn_117 = None
+        attn_119 = torch.nn.functional.softmax(attn_118, dim=-1)
+        attn_118 = None
+        attn_120 = torch.nn.functional.dropout(attn_119, p=0.0, training=False)
+        attn_119 = None
+        matmul_43 = attn_120.matmul(v_21)
+        attn_120 = v_21 = None
+        transpose_43 = matmul_43.transpose(1, 2)
+        matmul_43 = None
+        x_263 = transpose_43.reshape(4, 49, 512)
+        transpose_43 = None
+        x_264 = torch._C._nn.linear(
+            x_263,
+            l_self_modules_features_modules_5_modules_17_modules_attn_modules_proj_parameters_weight_,
+            l_self_modules_features_modules_5_modules_17_modules_attn_modules_proj_parameters_bias_,
+        )
+        x_263 = l_self_modules_features_modules_5_modules_17_modules_attn_modules_proj_parameters_weight_ = l_self_modules_features_modules_5_modules_17_modules_attn_modules_proj_parameters_bias_ = (None)
+        x_265 = torch.nn.functional.dropout(x_264, p=0.0, training=False)
+        x_264 = None
+        x_266 = x_265.view(1, 2, 2, 7, 7, 512)
+        x_265 = None
+        permute_99 = x_266.permute(0, 1, 3, 2, 4, 5)
+        x_266 = None
+        x_267 = permute_99.reshape(1, 14, 14, 512)
+        permute_99 = None
+        x_268 = torch.roll(x_267, shifts=(3, 3), dims=(1, 2))
+        x_267 = None
+        getitem_117 = x_268[
+            (
+                slice(None, None, None),
+                slice(None, 14, None),
+                slice(None, 14, None),
+                slice(None, None, None),
+            )
+        ]
+        x_268 = None
+        x_269 = getitem_117.contiguous()
+        getitem_117 = None
+        _log_api_usage_once_42 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_42 = None
+        x_270 = x_258 + x_269
+        x_258 = x_269 = None
+        layer_norm_46 = torch.nn.functional.layer_norm(
+            x_270,
+            (512,),
+            l_self_modules_features_modules_5_modules_17_modules_norm2_parameters_weight_,
+            l_self_modules_features_modules_5_modules_17_modules_norm2_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_5_modules_17_modules_norm2_parameters_weight_ = (
+            l_self_modules_features_modules_5_modules_17_modules_norm2_parameters_bias_
+        ) = None
+        input_109 = torch._C._nn.linear(
+            layer_norm_46,
+            l_self_modules_features_modules_5_modules_17_modules_mlp_modules_0_parameters_weight_,
+            l_self_modules_features_modules_5_modules_17_modules_mlp_modules_0_parameters_bias_,
+        )
+        layer_norm_46 = l_self_modules_features_modules_5_modules_17_modules_mlp_modules_0_parameters_weight_ = l_self_modules_features_modules_5_modules_17_modules_mlp_modules_0_parameters_bias_ = (None)
+        input_110 = torch._C._nn.gelu(input_109, approximate="none")
+        input_109 = None
+        input_111 = torch.nn.functional.dropout(input_110, 0.0, False, False)
+        input_110 = None
+        input_112 = torch._C._nn.linear(
+            input_111,
+            l_self_modules_features_modules_5_modules_17_modules_mlp_modules_3_parameters_weight_,
+            l_self_modules_features_modules_5_modules_17_modules_mlp_modules_3_parameters_bias_,
+        )
+        input_111 = l_self_modules_features_modules_5_modules_17_modules_mlp_modules_3_parameters_weight_ = l_self_modules_features_modules_5_modules_17_modules_mlp_modules_3_parameters_bias_ = (None)
+        input_113 = torch.nn.functional.dropout(input_112, 0.0, False, False)
+        input_112 = None
+        _log_api_usage_once_43 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_43 = None
+        x_271 = x_270 + input_113
+        x_270 = input_113 = None
+        x_272 = torch._C._nn.pad(x_271, (0, 0, 0, 0, 0, 0), "constant", None)
+        x_271 = None
+        x0_2 = x_272[
+            (Ellipsis, slice(0, None, 2), slice(0, None, 2), slice(None, None, None))
+        ]
+        x1_2 = x_272[
+            (Ellipsis, slice(1, None, 2), slice(0, None, 2), slice(None, None, None))
+        ]
+        x2_2 = x_272[
+            (Ellipsis, slice(0, None, 2), slice(1, None, 2), slice(None, None, None))
+        ]
+        x3_2 = x_272[
+            (Ellipsis, slice(1, None, 2), slice(1, None, 2), slice(None, None, None))
+        ]
+        x_272 = None
+        x_273 = torch.cat([x0_2, x1_2, x2_2, x3_2], -1)
+        x0_2 = x1_2 = x2_2 = x3_2 = None
+        x_274 = torch.nn.functional.layer_norm(
+            x_273,
+            (2048,),
+            l_self_modules_features_modules_6_modules_norm_parameters_weight_,
+            l_self_modules_features_modules_6_modules_norm_parameters_bias_,
+            1e-05,
+        )
+        x_273 = (
+            l_self_modules_features_modules_6_modules_norm_parameters_weight_
+        ) = l_self_modules_features_modules_6_modules_norm_parameters_bias_ = None
+        x_275 = torch._C._nn.linear(
+            x_274,
+            l_self_modules_features_modules_6_modules_reduction_parameters_weight_,
+            None,
+        )
+        x_274 = (
+            l_self_modules_features_modules_6_modules_reduction_parameters_weight_
+        ) = None
+        layer_norm_48 = torch.nn.functional.layer_norm(
+            x_275,
+            (1024,),
+            l_self_modules_features_modules_7_modules_0_modules_norm1_parameters_weight_,
+            l_self_modules_features_modules_7_modules_0_modules_norm1_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_7_modules_0_modules_norm1_parameters_weight_ = (
+            l_self_modules_features_modules_7_modules_0_modules_norm1_parameters_bias_
+        ) = None
+        relative_position_bias_66 = l_self_modules_features_modules_7_modules_0_modules_attn_parameters_relative_position_bias_table_[
+            l_self_modules_features_modules_7_modules_0_modules_attn_buffers_relative_position_index_
+        ]
+        l_self_modules_features_modules_7_modules_0_modules_attn_parameters_relative_position_bias_table_ = l_self_modules_features_modules_7_modules_0_modules_attn_buffers_relative_position_index_ = (None)
+        relative_position_bias_67 = relative_position_bias_66.view(49, 49, -1)
+        relative_position_bias_66 = None
+        permute_100 = relative_position_bias_67.permute(2, 0, 1)
+        relative_position_bias_67 = None
+        contiguous_44 = permute_100.contiguous()
+        permute_100 = None
+        relative_position_bias_68 = contiguous_44.unsqueeze(0)
+        contiguous_44 = None
+        x_276 = torch._C._nn.pad(layer_norm_48, (0, 0, 0, 0, 0, 0), "constant", None)
+        layer_norm_48 = None
+        x_277 = x_276.view(1, 1, 7, 1, 7, 1024)
+        x_276 = None
+        permute_101 = x_277.permute(0, 1, 3, 2, 4, 5)
+        x_277 = None
+        x_278 = permute_101.reshape(1, 49, 1024)
+        permute_101 = None
+        qkv_44 = torch._C._nn.linear(
+            x_278,
+            l_self_modules_features_modules_7_modules_0_modules_attn_modules_qkv_parameters_weight_,
+            l_self_modules_features_modules_7_modules_0_modules_attn_modules_qkv_parameters_bias_,
+        )
+        x_278 = l_self_modules_features_modules_7_modules_0_modules_attn_modules_qkv_parameters_weight_ = l_self_modules_features_modules_7_modules_0_modules_attn_modules_qkv_parameters_bias_ = (None)
+        reshape_100 = qkv_44.reshape(1, 49, 3, 32, 32)
+        qkv_44 = None
+        qkv_45 = reshape_100.permute(2, 0, 3, 1, 4)
+        reshape_100 = None
+        q_44 = qkv_45[0]
+        k_22 = qkv_45[1]
+        v_22 = qkv_45[2]
+        qkv_45 = None
+        q_45 = q_44 * 0.1767766952966369
+        q_44 = None
+        transpose_44 = k_22.transpose(-2, -1)
+        k_22 = None
+        attn_121 = q_45.matmul(transpose_44)
+        q_45 = transpose_44 = None
+        attn_122 = attn_121 + relative_position_bias_68
+        attn_121 = relative_position_bias_68 = None
+        attn_123 = torch.nn.functional.softmax(attn_122, dim=-1)
+        attn_122 = None
+        attn_124 = torch.nn.functional.dropout(attn_123, p=0.0, training=False)
+        attn_123 = None
+        matmul_45 = attn_124.matmul(v_22)
+        attn_124 = v_22 = None
+        transpose_45 = matmul_45.transpose(1, 2)
+        matmul_45 = None
+        x_279 = transpose_45.reshape(1, 49, 1024)
+        transpose_45 = None
+        x_280 = torch._C._nn.linear(
+            x_279,
+            l_self_modules_features_modules_7_modules_0_modules_attn_modules_proj_parameters_weight_,
+            l_self_modules_features_modules_7_modules_0_modules_attn_modules_proj_parameters_bias_,
+        )
+        x_279 = l_self_modules_features_modules_7_modules_0_modules_attn_modules_proj_parameters_weight_ = l_self_modules_features_modules_7_modules_0_modules_attn_modules_proj_parameters_bias_ = (None)
+        x_281 = torch.nn.functional.dropout(x_280, p=0.0, training=False)
+        x_280 = None
+        x_282 = x_281.view(1, 1, 1, 7, 7, 1024)
+        x_281 = None
+        permute_103 = x_282.permute(0, 1, 3, 2, 4, 5)
+        x_282 = None
+        x_283 = permute_103.reshape(1, 7, 7, 1024)
+        permute_103 = None
+        getitem_126 = x_283[
+            (
+                slice(None, None, None),
+                slice(None, 7, None),
+                slice(None, 7, None),
+                slice(None, None, None),
+            )
+        ]
+        x_283 = None
+        x_284 = getitem_126.contiguous()
+        getitem_126 = None
+        _log_api_usage_once_44 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_44 = None
+        x_285 = x_275 + x_284
+        x_275 = x_284 = None
+        layer_norm_49 = torch.nn.functional.layer_norm(
+            x_285,
+            (1024,),
+            l_self_modules_features_modules_7_modules_0_modules_norm2_parameters_weight_,
+            l_self_modules_features_modules_7_modules_0_modules_norm2_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_7_modules_0_modules_norm2_parameters_weight_ = (
+            l_self_modules_features_modules_7_modules_0_modules_norm2_parameters_bias_
+        ) = None
+        input_114 = torch._C._nn.linear(
+            layer_norm_49,
+            l_self_modules_features_modules_7_modules_0_modules_mlp_modules_0_parameters_weight_,
+            l_self_modules_features_modules_7_modules_0_modules_mlp_modules_0_parameters_bias_,
+        )
+        layer_norm_49 = l_self_modules_features_modules_7_modules_0_modules_mlp_modules_0_parameters_weight_ = l_self_modules_features_modules_7_modules_0_modules_mlp_modules_0_parameters_bias_ = (None)
+        input_115 = torch._C._nn.gelu(input_114, approximate="none")
+        input_114 = None
+        input_116 = torch.nn.functional.dropout(input_115, 0.0, False, False)
+        input_115 = None
+        input_117 = torch._C._nn.linear(
+            input_116,
+            l_self_modules_features_modules_7_modules_0_modules_mlp_modules_3_parameters_weight_,
+            l_self_modules_features_modules_7_modules_0_modules_mlp_modules_3_parameters_bias_,
+        )
+        input_116 = l_self_modules_features_modules_7_modules_0_modules_mlp_modules_3_parameters_weight_ = l_self_modules_features_modules_7_modules_0_modules_mlp_modules_3_parameters_bias_ = (None)
+        input_118 = torch.nn.functional.dropout(input_117, 0.0, False, False)
+        input_117 = None
+        _log_api_usage_once_45 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_45 = None
+        x_286 = x_285 + input_118
+        x_285 = input_118 = None
+        layer_norm_50 = torch.nn.functional.layer_norm(
+            x_286,
+            (1024,),
+            l_self_modules_features_modules_7_modules_1_modules_norm1_parameters_weight_,
+            l_self_modules_features_modules_7_modules_1_modules_norm1_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_7_modules_1_modules_norm1_parameters_weight_ = (
+            l_self_modules_features_modules_7_modules_1_modules_norm1_parameters_bias_
+        ) = None
+        relative_position_bias_69 = l_self_modules_features_modules_7_modules_1_modules_attn_parameters_relative_position_bias_table_[
+            l_self_modules_features_modules_7_modules_1_modules_attn_buffers_relative_position_index_
+        ]
+        l_self_modules_features_modules_7_modules_1_modules_attn_parameters_relative_position_bias_table_ = l_self_modules_features_modules_7_modules_1_modules_attn_buffers_relative_position_index_ = (None)
+        relative_position_bias_70 = relative_position_bias_69.view(49, 49, -1)
+        relative_position_bias_69 = None
+        permute_104 = relative_position_bias_70.permute(2, 0, 1)
+        relative_position_bias_70 = None
+        contiguous_46 = permute_104.contiguous()
+        permute_104 = None
+        relative_position_bias_71 = contiguous_46.unsqueeze(0)
+        contiguous_46 = None
+        x_287 = torch._C._nn.pad(layer_norm_50, (0, 0, 0, 0, 0, 0), "constant", None)
+        layer_norm_50 = None
+        x_288 = x_287.view(1, 1, 7, 1, 7, 1024)
+        x_287 = None
+        permute_105 = x_288.permute(0, 1, 3, 2, 4, 5)
+        x_288 = None
+        x_289 = permute_105.reshape(1, 49, 1024)
+        permute_105 = None
+        qkv_46 = torch._C._nn.linear(
+            x_289,
+            l_self_modules_features_modules_7_modules_1_modules_attn_modules_qkv_parameters_weight_,
+            l_self_modules_features_modules_7_modules_1_modules_attn_modules_qkv_parameters_bias_,
+        )
+        x_289 = l_self_modules_features_modules_7_modules_1_modules_attn_modules_qkv_parameters_weight_ = l_self_modules_features_modules_7_modules_1_modules_attn_modules_qkv_parameters_bias_ = (None)
+        reshape_104 = qkv_46.reshape(1, 49, 3, 32, 32)
+        qkv_46 = None
+        qkv_47 = reshape_104.permute(2, 0, 3, 1, 4)
+        reshape_104 = None
+        q_46 = qkv_47[0]
+        k_23 = qkv_47[1]
+        v_23 = qkv_47[2]
+        qkv_47 = None
+        q_47 = q_46 * 0.1767766952966369
+        q_46 = None
+        transpose_46 = k_23.transpose(-2, -1)
+        k_23 = None
+        attn_125 = q_47.matmul(transpose_46)
+        q_47 = transpose_46 = None
+        attn_126 = attn_125 + relative_position_bias_71
+        attn_125 = relative_position_bias_71 = None
+        attn_127 = torch.nn.functional.softmax(attn_126, dim=-1)
+        attn_126 = None
+        attn_128 = torch.nn.functional.dropout(attn_127, p=0.0, training=False)
+        attn_127 = None
+        matmul_47 = attn_128.matmul(v_23)
+        attn_128 = v_23 = None
+        transpose_47 = matmul_47.transpose(1, 2)
+        matmul_47 = None
+        x_290 = transpose_47.reshape(1, 49, 1024)
+        transpose_47 = None
+        x_291 = torch._C._nn.linear(
+            x_290,
+            l_self_modules_features_modules_7_modules_1_modules_attn_modules_proj_parameters_weight_,
+            l_self_modules_features_modules_7_modules_1_modules_attn_modules_proj_parameters_bias_,
+        )
+        x_290 = l_self_modules_features_modules_7_modules_1_modules_attn_modules_proj_parameters_weight_ = l_self_modules_features_modules_7_modules_1_modules_attn_modules_proj_parameters_bias_ = (None)
+        x_292 = torch.nn.functional.dropout(x_291, p=0.0, training=False)
+        x_291 = None
+        x_293 = x_292.view(1, 1, 1, 7, 7, 1024)
+        x_292 = None
+        permute_107 = x_293.permute(0, 1, 3, 2, 4, 5)
+        x_293 = None
+        x_294 = permute_107.reshape(1, 7, 7, 1024)
+        permute_107 = None
+        getitem_131 = x_294[
+            (
+                slice(None, None, None),
+                slice(None, 7, None),
+                slice(None, 7, None),
+                slice(None, None, None),
+            )
+        ]
+        x_294 = None
+        x_295 = getitem_131.contiguous()
+        getitem_131 = None
+        _log_api_usage_once_46 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_46 = None
+        x_296 = x_286 + x_295
+        x_286 = x_295 = None
+        layer_norm_51 = torch.nn.functional.layer_norm(
+            x_296,
+            (1024,),
+            l_self_modules_features_modules_7_modules_1_modules_norm2_parameters_weight_,
+            l_self_modules_features_modules_7_modules_1_modules_norm2_parameters_bias_,
+            1e-05,
+        )
+        l_self_modules_features_modules_7_modules_1_modules_norm2_parameters_weight_ = (
+            l_self_modules_features_modules_7_modules_1_modules_norm2_parameters_bias_
+        ) = None
+        input_119 = torch._C._nn.linear(
+            layer_norm_51,
+            l_self_modules_features_modules_7_modules_1_modules_mlp_modules_0_parameters_weight_,
+            l_self_modules_features_modules_7_modules_1_modules_mlp_modules_0_parameters_bias_,
+        )
+        layer_norm_51 = l_self_modules_features_modules_7_modules_1_modules_mlp_modules_0_parameters_weight_ = l_self_modules_features_modules_7_modules_1_modules_mlp_modules_0_parameters_bias_ = (None)
+        input_120 = torch._C._nn.gelu(input_119, approximate="none")
+        input_119 = None
+        input_121 = torch.nn.functional.dropout(input_120, 0.0, False, False)
+        input_120 = None
+        input_122 = torch._C._nn.linear(
+            input_121,
+            l_self_modules_features_modules_7_modules_1_modules_mlp_modules_3_parameters_weight_,
+            l_self_modules_features_modules_7_modules_1_modules_mlp_modules_3_parameters_bias_,
+        )
+        input_121 = l_self_modules_features_modules_7_modules_1_modules_mlp_modules_3_parameters_weight_ = l_self_modules_features_modules_7_modules_1_modules_mlp_modules_3_parameters_bias_ = (None)
+        input_123 = torch.nn.functional.dropout(input_122, 0.0, False, False)
+        input_122 = None
+        _log_api_usage_once_47 = torch._C._log_api_usage_once(
+            "torchvision.ops.stochastic_depth.stochastic_depth"
+        )
+        _log_api_usage_once_47 = None
+        x_297 = x_296 + input_123
+        x_296 = input_123 = None
+        x_298 = torch.nn.functional.layer_norm(
+            x_297,
+            (1024,),
+            l_self_modules_norm_parameters_weight_,
+            l_self_modules_norm_parameters_bias_,
+            1e-05,
+        )
+        x_297 = (
+            l_self_modules_norm_parameters_weight_
+        ) = l_self_modules_norm_parameters_bias_ = None
+        x_299 = torch.permute(x_298, [0, 3, 1, 2])
+        x_298 = None
+        x_300 = torch.nn.functional.adaptive_avg_pool2d(x_299, 1)
+        x_299 = None
+        x_301 = x_300.flatten(1, -1)
+        x_300 = None
+        x_302 = torch._C._nn.linear(
+            x_301,
+            l_self_modules_head_parameters_weight_,
+            l_self_modules_head_parameters_bias_,
+        )
+        x_301 = (
+            l_self_modules_head_parameters_weight_
+        ) = l_self_modules_head_parameters_bias_ = None
+        return (x_302,)
diff --git a/samples/torchvision/swin_b/weight_meta.py b/samples/torchvision/swin_b/weight_meta.py
new file mode 100644
index 00000000..3677b3cd
--- /dev/null
+++ b/samples/torchvision/swin_b/weight_meta.py
@@ -0,0 +1,70796 @@
+class Program_weight_tensor_meta_L_self_modules_features_modules_0_modules_0_parameters_weight_:
+    name = "L_self_modules_features_modules_0_modules_0_parameters_weight_"
+    shape = [128, 3, 4, 4]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.001
+    std = 0.059
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_0_modules_0_parameters_bias_:
+    name = "L_self_modules_features_modules_0_modules_0_parameters_bias_"
+    shape = [128]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.015
+    std = 0.258
+    data = [
+        -0.020580,
+        0.283728,
+        -0.200901,
+        0.002597,
+        -0.017654,
+        0.085659,
+        0.141164,
+        0.005762,
+        0.009549,
+        0.041488,
+        -0.054247,
+        -0.007034,
+        -0.004942,
+        -0.124063,
+        0.050179,
+        0.005669,
+        0.678892,
+        -0.057096,
+        -0.403960,
+        0.044616,
+        -0.000154,
+        0.069063,
+        -0.042219,
+        -0.030025,
+        -0.012469,
+        -0.639497,
+        0.148467,
+        0.203267,
+        -0.059786,
+        -0.007602,
+        -0.344942,
+        -0.001046,
+        0.179056,
+        -0.016475,
+        0.233868,
+        -0.051409,
+        -0.130037,
+        0.055038,
+        0.055872,
+        0.555678,
+        -0.072226,
+        0.000244,
+        -0.045779,
+        -0.288840,
+        -0.068376,
+        0.416677,
+        -0.028118,
+        -0.018367,
+        -0.010451,
+        -0.139688,
+        -0.330399,
+        0.114952,
+        0.297697,
+        -1.493450,
+        -0.082445,
+        0.189304,
+        0.089631,
+        0.143272,
+        0.169320,
+        0.086497,
+        -0.330648,
+        -0.711248,
+        -0.114825,
+        0.030978,
+        0.489761,
+        0.077791,
+        0.528010,
+        0.358995,
+        -0.003403,
+        0.034025,
+        0.411907,
+        0.020727,
+        0.391341,
+        0.426885,
+        -0.006556,
+        0.259307,
+        -0.004902,
+        0.413569,
+        -0.279905,
+        -0.042706,
+        -0.068479,
+        -0.064994,
+        -0.019712,
+        -0.104584,
+        0.017176,
+        0.135475,
+        -0.003532,
+        0.054377,
+        -0.038423,
+        0.081107,
+        -0.006996,
+        -0.007746,
+        -0.027567,
+        0.533443,
+        0.120575,
+        0.028548,
+        -0.040365,
+        -0.030292,
+        -0.426608,
+        -0.405708,
+        0.054390,
+        0.009608,
+        0.005212,
+        0.170829,
+        -0.093724,
+        -0.123437,
+        -0.035557,
+        -0.180954,
+        -0.045168,
+        -0.023025,
+        0.737591,
+        -0.024895,
+        0.007984,
+        -0.023833,
+        -0.195601,
+        0.000503,
+        -0.035144,
+        0.059907,
+        0.164966,
+        0.233337,
+        0.065295,
+        0.154189,
+        0.023660,
+        -0.404452,
+        0.004371,
+        0.195887,
+        -0.020193,
+        -0.012400,
+    ]
+
+
+class Program_weight_tensor_meta_L_x_:
+    name = "L_x_"
+    shape = [1, 3, 224, 224]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.219
+    std = 1.286
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_0_modules_2_parameters_weight_:
+    name = "L_self_modules_features_modules_0_modules_2_parameters_weight_"
+    shape = [128]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 1.350
+    std = 1.451
+    data = [
+        0.013831,
+        0.637008,
+        0.762892,
+        0.004759,
+        0.041526,
+        4.104910,
+        2.589788,
+        -0.007864,
+        4.952684,
+        3.435405,
+        0.001165,
+        0.006605,
+        -0.000092,
+        1.164400,
+        4.220786,
+        -0.008095,
+        0.802579,
+        3.958430,
+        1.113031,
+        4.282197,
+        4.302985,
+        3.185673,
+        0.005541,
+        -0.003258,
+        3.595756,
+        0.409415,
+        1.011467,
+        0.245593,
+        -0.003285,
+        -0.002434,
+        1.129898,
+        2.284710,
+        1.267558,
+        -0.000427,
+        2.536737,
+        0.688992,
+        2.265677,
+        2.129104,
+        2.289809,
+        0.947268,
+        0.083989,
+        3.960451,
+        3.005108,
+        0.401045,
+        -0.003746,
+        0.763921,
+        0.022215,
+        -0.007784,
+        -0.002503,
+        2.291264,
+        0.264444,
+        0.086559,
+        1.060535,
+        0.424510,
+        1.666142,
+        0.261389,
+        1.206193,
+        0.413032,
+        2.879162,
+        3.921856,
+        1.024815,
+        0.795910,
+        3.488918,
+        0.014565,
+        0.463532,
+        0.146871,
+        1.761893,
+        0.429222,
+        0.005295,
+        3.545580,
+        1.234580,
+        3.844336,
+        0.923418,
+        1.258916,
+        0.064092,
+        1.494644,
+        3.684413,
+        1.251226,
+        0.591174,
+        0.025457,
+        0.014082,
+        0.003713,
+        -0.006763,
+        1.048019,
+        -0.006058,
+        3.280809,
+        3.417600,
+        3.195660,
+        0.003812,
+        2.824533,
+        0.001064,
+        0.006360,
+        -0.003173,
+        1.345683,
+        0.811448,
+        0.790124,
+        -0.010692,
+        -0.000179,
+        0.841169,
+        0.468522,
+        2.597257,
+        0.053040,
+        0.002273,
+        2.253334,
+        3.267936,
+        0.758565,
+        4.266916,
+        2.701428,
+        0.001346,
+        3.399266,
+        0.635351,
+        4.044477,
+        1.676898,
+        0.010441,
+        1.622195,
+        -0.006256,
+        0.012630,
+        2.427275,
+        3.687324,
+        0.248517,
+        3.905146,
+        0.720571,
+        -0.004539,
+        0.602207,
+        3.786660,
+        0.568522,
+        2.389579,
+        -0.002016,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_0_modules_2_parameters_bias_:
+    name = "L_self_modules_features_modules_0_modules_2_parameters_bias_"
+    shape = [128]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.005
+    std = 0.519
+    data = [
+        0.144745,
+        -0.140745,
+        0.194364,
+        -0.004600,
+        -0.138561,
+        -0.581273,
+        -0.741762,
+        0.731870,
+        0.166554,
+        -0.055712,
+        0.112267,
+        -0.146797,
+        -0.367981,
+        0.503299,
+        -0.252442,
+        -0.234506,
+        -0.841663,
+        0.786643,
+        0.553642,
+        -0.229787,
+        0.231830,
+        -0.351532,
+        0.101038,
+        0.096268,
+        0.374533,
+        -0.101204,
+        -0.272942,
+        0.071080,
+        -0.565241,
+        0.573947,
+        0.824128,
+        0.073328,
+        -0.422685,
+        -0.534177,
+        -1.326350,
+        0.488909,
+        0.743192,
+        -0.150146,
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+        -0.883193,
+        0.064852,
+        0.206954,
+        0.496096,
+        0.207549,
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+        0.106675,
+        0.077038,
+        0.506152,
+        1.004090,
+        -0.053062,
+        0.210754,
+        -0.399033,
+        0.361698,
+        0.410892,
+        -0.004187,
+        -0.185187,
+        -0.088439,
+        -0.996925,
+        -0.588227,
+        0.704280,
+        0.682012,
+        1.225203,
+        -0.379202,
+        -0.224853,
+        0.018931,
+        -1.941448,
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+        0.135799,
+        -0.064926,
+        -0.890814,
+        0.038534,
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+        -0.847549,
+        -0.071068,
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+        0.272624,
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+        0.328645,
+        -0.190882,
+        -0.004482,
+        -0.023639,
+        0.051541,
+        0.157317,
+        0.616209,
+        -0.890248,
+        0.199709,
+        -0.165670,
+        0.096755,
+        -0.376148,
+        -0.578001,
+        0.065486,
+        0.607806,
+        -0.838462,
+        -0.149961,
+        0.304447,
+        0.012187,
+        0.058070,
+        0.736923,
+        0.102116,
+        -0.170577,
+        0.060661,
+        0.002917,
+        -0.746059,
+        0.973710,
+        0.400102,
+        0.618429,
+        1.435925,
+        0.137257,
+        0.379572,
+        -0.532001,
+        0.459274,
+        0.069428,
+        -0.058027,
+        0.673266,
+        0.319989,
+        0.157641,
+        -0.081561,
+        -1.239260,
+        0.084592,
+        -0.435022,
+        0.113326,
+        0.758974,
+        0.554982,
+        0.147681,
+        -0.242201,
+        0.122846,
+        -0.476249,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_1_modules_0_modules_norm1_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_1_modules_0_modules_norm1_parameters_weight_"
+    )
+    shape = [128]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.540
+    std = 0.384
+    data = [
+        -0.000455,
+        0.636927,
+        0.421201,
+        0.000911,
+        0.004164,
+        0.925538,
+        0.929882,
+        0.782923,
+        1.071179,
+        0.832254,
+        -0.000774,
+        0.004578,
+        0.587733,
+        0.583211,
+        1.112734,
+        0.736255,
+        0.372022,
+        0.945530,
+        0.433091,
+        1.235563,
+        1.063161,
+        0.820758,
+        -0.000144,
+        0.000292,
+        1.159422,
+        0.002387,
+        0.654063,
+        0.148768,
+        0.888046,
+        0.840463,
+        0.451401,
+        0.696563,
+        0.833702,
+        0.707086,
+        0.915228,
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+        0.306284,
+        1.163906,
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+        0.536370,
+        0.001773,
+        -0.000290,
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+        0.413151,
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+        0.522377,
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+        0.531795,
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+        1.002811,
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+        -0.000545,
+        -0.000692,
+        0.000488,
+        0.475395,
+        0.823089,
+        1.097577,
+        1.054580,
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+        -0.000292,
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+        0.758550,
+        0.000430,
+        1.006445,
+        0.448561,
+        0.791671,
+        0.835722,
+        0.000304,
+        0.000673,
+        0.256994,
+        0.150601,
+        0.651149,
+        -0.000326,
+        0.000036,
+        0.912516,
+        0.900752,
+        0.888753,
+        1.031178,
+        1.069879,
+        0.001150,
+        1.269834,
+        0.313422,
+        1.109916,
+        0.583353,
+        -0.000637,
+        0.338881,
+        0.036691,
+        0.000166,
+        0.756838,
+        0.947843,
+        0.174955,
+        0.968364,
+        1.033316,
+        0.988323,
+        0.570502,
+        1.113322,
+        0.639209,
+        0.535225,
+        0.632035,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_1_modules_0_modules_norm1_parameters_bias_:
+    name = "L_self_modules_features_modules_1_modules_0_modules_norm1_parameters_bias_"
+    shape = [128]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.020
+    std = 0.330
+    data = [
+        -0.000977,
+        -0.167493,
+        0.276346,
+        -0.000959,
+        0.001695,
+        -0.410063,
+        -0.333032,
+        -0.779972,
+        0.009523,
+        -0.176813,
+        0.000512,
+        0.001054,
+        0.391195,
+        0.093211,
+        -0.227495,
+        0.290699,
+        -0.488430,
+        0.358874,
+        0.435443,
+        -0.185334,
+        0.111981,
+        -0.139886,
+        0.000013,
+        -0.000225,
+        0.072405,
+        0.005232,
+        -0.085637,
+        -0.032974,
+        0.803078,
+        -0.633797,
+        0.421489,
+        0.157609,
+        -0.206550,
+        0.620936,
+        -0.567504,
+        0.063093,
+        0.435241,
+        -0.014810,
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+        -0.417699,
+        -0.004216,
+        0.098117,
+        0.252961,
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+        0.124681,
+        -0.296015,
+        -0.000142,
+        -0.000740,
+        -0.576258,
+        0.363040,
+        0.063215,
+        -0.001122,
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+        0.471975,
+        0.135329,
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+        -0.044272,
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+        0.436571,
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+        0.148037,
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+        -0.511027,
+        0.181769,
+        -0.177434,
+        0.000583,
+        -0.169890,
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+        0.000360,
+        -0.746273,
+        -0.569124,
+        -0.073485,
+        0.221284,
+        -0.000077,
+        0.000340,
+        0.223020,
+        0.163729,
+        -0.086496,
+        -0.000553,
+        0.000110,
+        -0.431853,
+        0.419709,
+        0.304022,
+        0.296533,
+        0.626267,
+        -0.000174,
+        0.291512,
+        -0.569449,
+        0.317143,
+        0.055817,
+        -0.000205,
+        0.290173,
+        -0.015864,
+        -0.000492,
+        -0.162840,
+        -0.681996,
+        -0.071671,
+        -0.225159,
+        0.067208,
+        -0.967494,
+        0.284219,
+        0.154134,
+        -0.148143,
+        0.162995,
+        0.525119,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_1_modules_0_modules_attn_parameters_relative_position_bias_table_:
+    name = "L_self_modules_features_modules_1_modules_0_modules_attn_parameters_relative_position_bias_table_"
+    shape = [169, 4]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.730
+    std = 1.361
+    data = [
+        -1.741705,
+        0.190783,
+        0.147001,
+        -0.264906,
+        -2.638818,
+        0.035614,
+        -0.450533,
+        -0.615050,
+        -2.734565,
+        0.107916,
+        -0.459494,
+        -0.443153,
+        -2.422251,
+        -0.083818,
+        -0.527001,
+        -0.469953,
+        -2.151413,
+        -0.050572,
+        -0.357551,
+        0.020042,
+        -1.994476,
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+        -0.320920,
+        0.042272,
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+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_1_modules_0_modules_attn_buffers_relative_position_index_:
+    name = "L_self_modules_features_modules_1_modules_0_modules_attn_buffers_relative_position_index_"
+    shape = [2401]
+    dtype = "torch.int64"
+    device = "cpu"
+    mean = None
+    std = None
+    min_val = 0
+    max_val = 168
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_1_modules_0_modules_attn_modules_qkv_parameters_weight_:
+    name = "L_self_modules_features_modules_1_modules_0_modules_attn_modules_qkv_parameters_weight_"
+    shape = [384, 128]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.066
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_1_modules_0_modules_attn_modules_proj_parameters_weight_:
+    name = "L_self_modules_features_modules_1_modules_0_modules_attn_modules_proj_parameters_weight_"
+    shape = [128, 128]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.001
+    std = 0.049
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_1_modules_0_modules_attn_modules_qkv_parameters_bias_:
+    name = "L_self_modules_features_modules_1_modules_0_modules_attn_modules_qkv_parameters_bias_"
+    shape = [384]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.044
+    std = 0.538
+    data = [
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+        0.168122,
+        -0.032300,
+        0.062668,
+        -0.020424,
+        0.027768,
+        0.039080,
+        -0.080782,
+        -0.074812,
+        0.063078,
+        -0.100462,
+        -0.071144,
+        -0.059622,
+        -0.417385,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_1_modules_0_modules_attn_modules_proj_parameters_bias_:
+    name = "L_self_modules_features_modules_1_modules_0_modules_attn_modules_proj_parameters_bias_"
+    shape = [128]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.004
+    std = 0.234
+    data = [
+        0.036188,
+        -0.094849,
+        0.219578,
+        0.037238,
+        0.032285,
+        -0.263463,
+        -0.157818,
+        -0.192003,
+        -0.078389,
+        -0.152703,
+        0.103323,
+        -0.278628,
+        0.241909,
+        0.189258,
+        -0.071631,
+        0.182422,
+        -0.534575,
+        0.145004,
+        0.457587,
+        -0.070426,
+        0.053350,
+        -0.037824,
+        0.109994,
+        0.003991,
+        0.038446,
+        -0.358703,
+        -0.129598,
+        0.052897,
+        0.297700,
+        -0.161226,
+        0.274197,
+        0.083280,
+        -0.150972,
+        0.211351,
+        -0.405380,
+        0.119186,
+        0.321870,
+        -0.011304,
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+        0.052193,
+        0.248195,
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+        -0.096060,
+        0.272791,
+        0.201105,
+        0.546132,
+        -0.218737,
+        0.303986,
+        0.078343,
+        0.107370,
+        -0.004942,
+        -0.055688,
+        -0.294721,
+        -0.198358,
+        0.321863,
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+        0.289947,
+        0.054576,
+        -0.166095,
+        0.080546,
+        -0.585638,
+        -0.168059,
+        0.270933,
+        0.009090,
+        -0.379970,
+        0.044202,
+        -0.382242,
+        -0.430671,
+        0.035526,
+        -0.263304,
+        -0.012855,
+        -0.496375,
+        0.160816,
+        0.085037,
+        0.063981,
+        -0.004401,
+        0.094551,
+        0.136546,
+        -0.117580,
+        -0.238051,
+        0.073605,
+        -0.003146,
+        0.171929,
+        -0.045552,
+        0.063876,
+        0.004268,
+        -0.377246,
+        -0.665899,
+        -0.108767,
+        0.131481,
+        0.025561,
+        0.091720,
+        0.443840,
+        0.234057,
+        -0.005975,
+        0.033365,
+        -0.004553,
+        -0.165267,
+        0.297967,
+        0.226578,
+        0.155123,
+        0.311288,
+        0.095010,
+        0.085657,
+        -0.505604,
+        0.139264,
+        0.091888,
+        0.067085,
+        0.331021,
+        0.368725,
+        0.108902,
+        -0.139850,
+        -0.332620,
+        -0.112259,
+        -0.115958,
+        -0.030379,
+        -0.152269,
+        0.252322,
+        0.049516,
+        -0.108799,
+        0.018095,
+        0.068003,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_1_modules_0_modules_norm2_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_1_modules_0_modules_norm2_parameters_weight_"
+    )
+    shape = [128]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 1.437
+    std = 0.693
+    data = [
+        0.699144,
+        1.720057,
+        0.942560,
+        1.622114,
+        0.678340,
+        2.097166,
+        2.438591,
+        0.865779,
+        2.141217,
+        1.469903,
+        1.269011,
+        0.367155,
+        1.283295,
+        0.916279,
+        2.590159,
+        2.747062,
+        0.943771,
+        1.758828,
+        1.390557,
+        3.090979,
+        2.256758,
+        1.530117,
+        1.687051,
+        0.921628,
+        2.916656,
+        0.196887,
+        1.279150,
+        0.717195,
+        1.238450,
+        1.127983,
+        1.061213,
+        1.601739,
+        1.775505,
+        0.922742,
+        2.095055,
+        2.141281,
+        1.398612,
+        0.806679,
+        1.282298,
+        1.104100,
+        1.430332,
+        2.757541,
+        1.400726,
+        0.779857,
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+        1.451258,
+        1.417742,
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+        1.208788,
+        2.095988,
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+        0.297756,
+        0.535151,
+        1.429982,
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+        1.152792,
+        0.733084,
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+        2.044807,
+        1.254164,
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+        1.016112,
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+        1.609431,
+        1.394249,
+        2.029410,
+        0.794553,
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+        1.067573,
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+        2.472822,
+        0.690231,
+        2.050425,
+        0.796286,
+        0.705831,
+        1.428653,
+        1.070331,
+        0.987458,
+        0.988854,
+        2.995563,
+        2.379684,
+        1.348223,
+        1.326488,
+        1.319769,
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+        1.817129,
+        1.154283,
+        1.219639,
+        1.790141,
+        1.884120,
+        1.721076,
+        1.216557,
+        0.726261,
+        0.301500,
+        1.046815,
+        0.922470,
+        1.701003,
+        2.024785,
+        2.235079,
+        2.374351,
+        2.384322,
+        2.799800,
+        1.512823,
+        3.080327,
+        1.383393,
+        2.868062,
+        1.076746,
+        1.119478,
+        0.831476,
+        0.000705,
+        0.850988,
+        1.707251,
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+        0.568905,
+        2.155008,
+        2.663144,
+        1.051841,
+        1.445961,
+        2.395052,
+        1.766310,
+        1.011225,
+        0.917861,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_1_modules_0_modules_norm2_parameters_bias_:
+    name = "L_self_modules_features_modules_1_modules_0_modules_norm2_parameters_bias_"
+    shape = [128]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.034
+    std = 0.408
+    data = [
+        -0.264827,
+        -0.315181,
+        0.351374,
+        -0.037266,
+        0.300989,
+        -0.248639,
+        -0.328666,
+        -0.885912,
+        0.080702,
+        -0.066931,
+        -0.452416,
+        0.347974,
+        0.524131,
+        -0.151769,
+        -0.261861,
+        -0.603067,
+        -0.492628,
+        0.237692,
+        0.715500,
+        -0.145260,
+        0.140622,
+        -0.190556,
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+        -1.103059,
+        0.110175,
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+        0.057562,
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+        0.171887,
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+        0.280246,
+        -0.380866,
+        0.201451,
+        0.408321,
+        0.139360,
+        0.409772,
+        -0.660739,
+        0.498390,
+        -0.363729,
+        0.280822,
+        0.043309,
+        -0.011797,
+        0.314607,
+        -0.001247,
+        -0.346931,
+        -0.228885,
+        -0.594073,
+        -0.108853,
+        -0.135953,
+        0.209181,
+        -1.199324,
+        0.209356,
+        0.135119,
+        -0.172851,
+        0.284601,
+        0.832223,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_1_modules_0_modules_mlp_modules_0_parameters_weight_:
+    name = "L_self_modules_features_modules_1_modules_0_modules_mlp_modules_0_parameters_weight_"
+    shape = [512, 128]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.047
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_1_modules_0_modules_mlp_modules_0_parameters_bias_:
+    name = "L_self_modules_features_modules_1_modules_0_modules_mlp_modules_0_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.377
+    std = 0.438
+    data = [
+        -1.224424,
+        -0.015013,
+        -0.064103,
+        -0.451532,
+        -0.035828,
+        -0.520463,
+        -0.244633,
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+        -1.041296,
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+        -0.010105,
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+        0.236610,
+        -0.018177,
+        -0.261963,
+        0.242706,
+        -0.310250,
+        -0.069712,
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+        -1.178740,
+        -1.277382,
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+        -0.023117,
+        -0.003309,
+        0.313721,
+        -0.420248,
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+        -0.032844,
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+        -1.230046,
+        -0.406921,
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+        -0.340911,
+        -1.437206,
+        -0.041855,
+        0.306896,
+        -0.442138,
+        0.175060,
+        -1.328219,
+        -0.597091,
+        -0.041059,
+        -0.032732,
+        -0.526555,
+        -0.482336,
+        -1.469741,
+        -0.292454,
+        -1.394519,
+        -0.348762,
+        -0.029525,
+        -0.046946,
+        -0.031920,
+        -0.034204,
+        -0.035755,
+        -0.881758,
+        -0.273702,
+        -0.346173,
+        -1.020725,
+        -0.341010,
+        -0.332423,
+        -0.009871,
+        -0.518477,
+        -0.951199,
+        -0.426070,
+        -0.017059,
+        -0.711489,
+        -1.252594,
+        -1.425516,
+        -0.864113,
+        -0.061351,
+        0.148206,
+        -0.587442,
+        -0.750120,
+        -0.024470,
+        -0.009346,
+        -1.131017,
+        -0.395441,
+        -0.148078,
+        -0.015086,
+        -0.002125,
+        -0.252762,
+        -0.020642,
+        -0.531119,
+        -0.003707,
+        -1.165674,
+        -1.069791,
+        -1.165857,
+        -0.377442,
+        -0.764402,
+        -0.641083,
+        -0.745756,
+        -0.003423,
+        0.005073,
+        0.752372,
+        -0.372899,
+        -0.885461,
+        0.404578,
+        -0.488498,
+        -0.496756,
+        -0.148498,
+        -1.434465,
+        -0.372342,
+        -0.548223,
+        -0.215891,
+        -0.493720,
+        -0.540343,
+        0.285634,
+        -0.026369,
+        0.007026,
+        -0.045677,
+        -0.216971,
+        -1.182544,
+        0.035042,
+        -0.025166,
+        -0.697004,
+        -0.449890,
+        -0.474790,
+        -0.265962,
+        -0.017335,
+        -1.291037,
+        -0.338097,
+        -0.010389,
+        -0.514961,
+        0.428586,
+        0.471865,
+        -0.531606,
+        -0.411343,
+        -0.733261,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_1_modules_0_modules_mlp_modules_3_parameters_weight_:
+    name = "L_self_modules_features_modules_1_modules_0_modules_mlp_modules_3_parameters_weight_"
+    shape = [128, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.041
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_1_modules_0_modules_mlp_modules_3_parameters_bias_:
+    name = "L_self_modules_features_modules_1_modules_0_modules_mlp_modules_3_parameters_bias_"
+    shape = [128]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.009
+    std = 0.290
+    data = [
+        0.643611,
+        0.015138,
+        0.061648,
+        0.172135,
+        -0.224563,
+        -0.006216,
+        -0.090354,
+        0.158778,
+        0.180695,
+        -0.022497,
+        0.236813,
+        -0.396697,
+        0.245919,
+        0.112779,
+        0.084725,
+        -0.234545,
+        -0.019625,
+        0.047927,
+        -0.298329,
+        -0.099700,
+        -0.007391,
+        0.274925,
+        -0.038233,
+        0.096618,
+        0.203936,
+        -1.284279,
+        -0.045209,
+        -0.130547,
+        -0.011644,
+        -0.058629,
+        0.104871,
+        -0.044506,
+        -0.175037,
+        0.345111,
+        -0.038542,
+        0.416113,
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+        0.235857,
+        0.001975,
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+        -0.065182,
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+        -0.101971,
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+        -0.001270,
+        0.940297,
+        -0.104203,
+        -2.036858,
+        0.143473,
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+        0.053419,
+        0.136167,
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+        0.010512,
+        0.113863,
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+        -0.178590,
+        0.142474,
+        0.044254,
+        0.052114,
+        -0.223210,
+        0.060532,
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+        0.069305,
+        -0.029648,
+        0.019839,
+        -0.066467,
+        -0.148982,
+        0.050665,
+        0.144755,
+        0.387880,
+        0.217433,
+        -0.044318,
+        -0.018250,
+        0.007865,
+        0.054162,
+        -0.436046,
+        0.053357,
+        -0.102173,
+        0.166488,
+        -0.025539,
+        -0.142587,
+        -0.183161,
+        0.161116,
+        -0.075702,
+        0.067763,
+        -0.066303,
+        0.186675,
+        0.214502,
+        0.014523,
+        -0.083160,
+        0.147865,
+        0.354968,
+        -0.009430,
+        0.216119,
+        0.019883,
+        0.310447,
+        -0.228738,
+        -0.047480,
+        -0.019455,
+        0.113528,
+        0.127695,
+        -0.033178,
+        -0.012452,
+        0.025766,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_1_modules_1_modules_norm1_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_1_modules_1_modules_norm1_parameters_weight_"
+    )
+    shape = [128]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.982
+    std = 0.250
+    data = [
+        0.753803,
+        0.708123,
+        1.259541,
+        0.948636,
+        0.619600,
+        1.123736,
+        0.944092,
+        0.975573,
+        0.973999,
+        0.941785,
+        1.073187,
+        0.429601,
+        1.095600,
+        1.232071,
+        1.070473,
+        0.922375,
+        0.896994,
+        1.126825,
+        0.845493,
+        1.209113,
+        1.186417,
+        1.163582,
+        1.213343,
+        0.992804,
+        1.076231,
+        0.257456,
+        0.889086,
+        1.107266,
+        1.477350,
+        1.464598,
+        1.177497,
+        0.766978,
+        1.166094,
+        1.202029,
+        0.998556,
+        0.843751,
+        1.059303,
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+        1.072546,
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+        0.893812,
+        1.134997,
+        0.853802,
+        1.162391,
+        1.157443,
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+        1.189487,
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+        0.982639,
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+        1.064042,
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+        1.013643,
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+        1.357681,
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+        1.008445,
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+        0.978420,
+        1.094805,
+        1.009269,
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+        0.908336,
+        1.124685,
+        0.729798,
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+        0.776760,
+        1.154428,
+        1.202088,
+        0.515624,
+        1.082690,
+        1.367812,
+        0.568450,
+        0.947961,
+        1.311373,
+        1.139270,
+        0.875453,
+        1.232936,
+        0.900058,
+        1.218558,
+        0.902524,
+        0.862626,
+        0.903322,
+        1.170640,
+        0.404887,
+        1.239393,
+        1.160877,
+        0.927741,
+        0.364621,
+        1.046142,
+        1.066724,
+        1.016487,
+        1.131979,
+        0.979784,
+        1.104127,
+        1.126330,
+        1.399290,
+        1.247277,
+        0.879547,
+        1.036427,
+        1.295985,
+        0.678212,
+        1.158893,
+        0.986076,
+        0.542160,
+        1.075578,
+        0.954148,
+        1.370283,
+        0.340387,
+        1.134494,
+        1.084512,
+        0.851614,
+        0.877392,
+        0.928240,
+        0.869818,
+        0.961308,
+        1.186606,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_1_modules_1_modules_norm1_parameters_bias_:
+    name = "L_self_modules_features_modules_1_modules_1_modules_norm1_parameters_bias_"
+    shape = [128]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.031
+    std = 0.324
+    data = [
+        -0.487092,
+        0.038029,
+        0.112921,
+        0.302933,
+        0.242964,
+        -0.158509,
+        -0.189842,
+        -0.412553,
+        -0.152508,
+        -0.000505,
+        -0.267733,
+        0.818347,
+        -0.082813,
+        -0.081673,
+        -0.075513,
+        0.342081,
+        -0.371919,
+        0.000580,
+        0.605146,
+        -0.125722,
+        0.108985,
+        -0.277045,
+        -0.079857,
+        -0.355836,
+        -0.126059,
+        1.077038,
+        0.022730,
+        -0.033045,
+        0.387416,
+        -0.079170,
+        0.232356,
+        0.073112,
+        -0.050306,
+        0.366640,
+        -0.080456,
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+        0.277884,
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+        -0.322405,
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+        0.116688,
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+        0.404409,
+        -0.311765,
+        0.153484,
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+        0.063188,
+        0.237211,
+        0.490335,
+        -0.864654,
+        -0.218624,
+        1.363070,
+        -0.124335,
+        -0.478248,
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+        -0.264182,
+        -0.574649,
+        -0.112053,
+        0.146110,
+        0.317630,
+        0.138924,
+        0.302330,
+        -0.094174,
+        0.343445,
+        0.087287,
+        -0.317471,
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+        -0.127062,
+        -0.399172,
+        -0.299488,
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+        -0.196166,
+        -0.041190,
+        0.247616,
+        -0.035101,
+        -0.108715,
+        0.055489,
+        0.218259,
+        -0.888394,
+        -0.205863,
+        -0.066007,
+        -0.184780,
+        0.100153,
+        0.077929,
+        0.206356,
+        -0.211143,
+        -0.553864,
+        -0.498093,
+        -0.232275,
+        0.083657,
+        -0.052431,
+        0.144934,
+        0.167574,
+        0.234461,
+        -0.131660,
+        0.189205,
+        0.011364,
+        0.064906,
+        0.270517,
+        0.362544,
+        0.042575,
+        0.210268,
+        -0.032377,
+        0.080142,
+        -0.967836,
+        -0.134171,
+        -0.027613,
+        0.074235,
+        -0.022708,
+        -0.384959,
+        -0.029753,
+        -0.369822,
+        0.008574,
+        -0.214062,
+        0.194222,
+        0.004303,
+        -0.402314,
+        0.043333,
+        -0.207267,
+        -0.361274,
+        0.098130,
+        0.342355,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_1_modules_1_modules_attn_parameters_relative_position_bias_table_:
+    name = "L_self_modules_features_modules_1_modules_1_modules_attn_parameters_relative_position_bias_table_"
+    shape = [169, 4]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.743
+    std = 1.264
+    data = [
+        -0.178853,
+        -0.499783,
+        0.156655,
+        -0.679885,
+        -0.841402,
+        -0.775532,
+        0.086460,
+        -1.839953,
+        -1.193528,
+        -0.518425,
+        0.091586,
+        -2.604619,
+        -1.417058,
+        -1.012750,
+        0.023349,
+        -2.538851,
+        -0.733879,
+        -0.509697,
+        0.344351,
+        -2.944449,
+        -0.395998,
+        -0.288700,
+        0.027476,
+        -2.766650,
+        -0.156383,
+        0.123967,
+        0.902094,
+        -1.653412,
+        -0.303818,
+        -0.483507,
+        -0.158971,
+        -3.043969,
+        -0.826558,
+        -0.601322,
+        0.004623,
+        -2.808849,
+        -1.054931,
+        -0.774867,
+        -0.145606,
+        -2.707262,
+        -1.221185,
+        -0.696695,
+        -0.244802,
+        -1.916722,
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+        -0.490797,
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+        -1.436331,
+        -0.346290,
+        -0.593341,
+        0.065419,
+        -0.877202,
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+        -1.062018,
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+        -1.241839,
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+        -3.876739,
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+        -0.850209,
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+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_1_modules_1_modules_attn_buffers_relative_position_index_:
+    name = "L_self_modules_features_modules_1_modules_1_modules_attn_buffers_relative_position_index_"
+    shape = [2401]
+    dtype = "torch.int64"
+    device = "cpu"
+    mean = None
+    std = None
+    min_val = 0
+    max_val = 168
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_1_modules_1_modules_attn_modules_qkv_parameters_weight_:
+    name = "L_self_modules_features_modules_1_modules_1_modules_attn_modules_qkv_parameters_weight_"
+    shape = [384, 128]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.064
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_1_modules_1_modules_attn_modules_proj_parameters_weight_:
+    name = "L_self_modules_features_modules_1_modules_1_modules_attn_modules_proj_parameters_weight_"
+    shape = [128, 128]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.048
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_1_modules_1_modules_attn_modules_qkv_parameters_bias_:
+    name = "L_self_modules_features_modules_1_modules_1_modules_attn_modules_qkv_parameters_bias_"
+    shape = [384]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.039
+    std = 0.448
+    data = [
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+        -0.326500,
+        0.534873,
+        0.211063,
+        -0.334013,
+        0.359239,
+        0.068942,
+        0.167828,
+        -0.059830,
+        0.221722,
+        -0.054438,
+        -0.271511,
+        0.396793,
+        -0.226599,
+        0.420572,
+        0.531139,
+        0.373880,
+        0.040723,
+        0.137020,
+        -0.108892,
+        0.088926,
+        -0.052113,
+        -0.020201,
+        -0.067023,
+        -0.050671,
+        -0.146382,
+        -0.079104,
+        -0.062551,
+        -0.100550,
+        -0.061377,
+        -0.165983,
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+        0.037666,
+        0.041613,
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+        -0.118773,
+        0.030575,
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+        0.069676,
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+        0.030647,
+        0.237430,
+        0.113791,
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+        -0.161107,
+        -0.009193,
+        -0.079316,
+        0.006763,
+        -0.043857,
+        -0.066471,
+        -0.005003,
+        -0.295831,
+        -0.080499,
+        0.002113,
+        0.004939,
+        -0.161611,
+        -0.119188,
+        -0.010660,
+        0.014233,
+        0.017603,
+        0.164624,
+        0.016702,
+        -0.004542,
+        -0.013096,
+        -0.259144,
+        -0.394028,
+        -0.005152,
+        0.022367,
+        -0.053958,
+        -0.065626,
+        0.013102,
+        -0.012606,
+        0.026785,
+        0.056719,
+        0.075866,
+        0.080438,
+        0.026124,
+        0.038848,
+        0.159733,
+        0.061556,
+        0.018991,
+        0.068112,
+        0.065922,
+        0.027115,
+        -0.001870,
+        0.004562,
+        0.006045,
+        -0.086105,
+        0.032795,
+        0.033319,
+        0.048926,
+        0.038170,
+        -0.118687,
+        0.031324,
+        0.006755,
+        -0.090100,
+        -0.011238,
+        0.005395,
+        -0.069856,
+        0.000027,
+        -0.006618,
+        -0.034668,
+        -0.017400,
+        0.053071,
+        0.027798,
+        -0.093333,
+        -0.029496,
+        0.003656,
+        0.006033,
+        0.062618,
+        0.040409,
+        0.063874,
+        -0.009634,
+        0.041789,
+        0.010051,
+        -0.062274,
+        0.015204,
+        0.000824,
+        0.080017,
+        -0.006640,
+        -0.005083,
+        0.036085,
+        0.039595,
+        0.012798,
+        -0.022330,
+        -0.016281,
+        0.069104,
+        0.081245,
+        -0.097473,
+        0.170994,
+        0.033677,
+        -0.129103,
+        0.005186,
+        0.129884,
+        -0.130746,
+        0.021984,
+        -0.044960,
+        -0.082357,
+        0.011510,
+        0.026119,
+        -0.019176,
+        -0.114479,
+        0.047480,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_1_modules_1_modules_attn_modules_proj_parameters_bias_:
+    name = "L_self_modules_features_modules_1_modules_1_modules_attn_modules_proj_parameters_bias_"
+    shape = [128]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.008
+    std = 0.220
+    data = [
+        -0.224352,
+        -0.060339,
+        -0.156330,
+        0.293954,
+        -0.151910,
+        0.005990,
+        0.123539,
+        -0.007662,
+        -0.042951,
+        0.078126,
+        -0.127208,
+        0.203939,
+        -0.041659,
+        -0.098745,
+        -0.130592,
+        -0.172853,
+        -0.237763,
+        0.141143,
+        0.244996,
+        -0.184081,
+        0.268703,
+        -0.361767,
+        0.102526,
+        -0.034463,
+        -0.081906,
+        -0.206297,
+        0.082157,
+        0.136744,
+        0.192336,
+        -0.134567,
+        0.020096,
+        0.041399,
+        -0.043412,
+        -0.131444,
+        0.459184,
+        -0.315633,
+        0.324983,
+        0.152167,
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+        0.138344,
+        -0.306235,
+        0.068429,
+        0.090502,
+        -0.137980,
+        0.243873,
+        -0.223636,
+        0.178773,
+        0.100537,
+        0.136204,
+        0.021413,
+        0.614323,
+        -0.019796,
+        -0.119756,
+        0.035828,
+        0.016781,
+        -0.672939,
+        -0.122817,
+        -0.196534,
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+        0.055565,
+        0.094906,
+        0.109048,
+        -0.027327,
+        0.119688,
+        -0.008185,
+        0.166989,
+        0.188308,
+        -0.217760,
+        -0.681856,
+        -0.175286,
+        0.066486,
+        -0.204152,
+        -0.102139,
+        -0.325997,
+        -0.330191,
+        -0.128115,
+        0.327267,
+        -0.128143,
+        -0.087660,
+        -0.109478,
+        -0.249981,
+        -0.107360,
+        0.207217,
+        0.129723,
+        -0.459453,
+        -0.047818,
+        0.146869,
+        -0.100251,
+        0.420183,
+        0.248621,
+        0.285455,
+        -0.560189,
+        0.103311,
+        -0.137732,
+        -0.054012,
+        0.045583,
+        0.110875,
+        0.012906,
+        0.115505,
+        0.334174,
+        0.056337,
+        0.226198,
+        -0.040820,
+        0.004051,
+        0.176408,
+        0.282380,
+        -0.088967,
+        0.052022,
+        0.133793,
+        0.145754,
+        -0.302394,
+        -0.199416,
+        0.016235,
+        0.346047,
+        0.033395,
+        -0.342976,
+        0.072501,
+        -0.133472,
+        0.028658,
+        0.002156,
+        0.302271,
+        0.188509,
+        -0.064473,
+        0.005405,
+        -0.206533,
+        -0.217941,
+        0.319481,
+        0.265920,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_1_modules_1_modules_norm2_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_1_modules_1_modules_norm2_parameters_weight_"
+    )
+    shape = [128]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 1.669
+    std = 0.471
+    data = [
+        1.179724,
+        1.157806,
+        2.132921,
+        1.681072,
+        0.933810,
+        2.023945,
+        1.438680,
+        1.356538,
+        2.223775,
+        2.163745,
+        1.541576,
+        -0.000472,
+        1.776677,
+        2.192474,
+        2.182523,
+        1.347103,
+        1.666742,
+        2.290754,
+        1.070349,
+        1.804837,
+        2.041730,
+        2.378255,
+        1.778898,
+        1.736977,
+        1.903996,
+        0.231723,
+        1.663776,
+        1.730137,
+        2.084669,
+        2.022482,
+        2.182508,
+        1.850341,
+        1.823687,
+        1.452598,
+        2.126363,
+        1.462134,
+        2.119084,
+        1.562729,
+        2.001127,
+        1.608621,
+        1.836391,
+        1.741452,
+        1.690535,
+        1.695327,
+        1.670047,
+        1.881227,
+        2.000031,
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+        1.755580,
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+        0.261155,
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+        0.462841,
+        1.667891,
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+        1.706479,
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+        2.214369,
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+        1.221164,
+        2.054900,
+        2.046077,
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+        0.755386,
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+        1.712273,
+        1.467580,
+        1.440788,
+        1.511255,
+        1.834057,
+        1.637431,
+        0.883228,
+        1.798827,
+        1.787521,
+        1.342061,
+        1.647550,
+        2.245140,
+        1.937968,
+        1.687417,
+        1.762799,
+        1.749693,
+        1.845734,
+        1.616113,
+        0.647392,
+        1.019875,
+        1.883127,
+        0.955892,
+        2.013299,
+        1.857387,
+        1.861519,
+        0.332463,
+        1.944183,
+        1.820854,
+        1.700249,
+        1.827568,
+        1.930690,
+        1.753747,
+        2.073457,
+        2.023046,
+        2.081965,
+        1.696357,
+        0.783275,
+        2.065463,
+        1.438564,
+        1.615392,
+        1.740698,
+        0.855376,
+        1.503208,
+        1.922706,
+        2.468249,
+        0.565121,
+        2.305994,
+        2.030686,
+        1.522674,
+        1.784410,
+        1.602033,
+        1.763028,
+        2.145881,
+        1.923457,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_1_modules_1_modules_norm2_parameters_bias_:
+    name = "L_self_modules_features_modules_1_modules_1_modules_norm2_parameters_bias_"
+    shape = [128]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.041
+    std = 0.295
+    data = [
+        -0.368006,
+        0.151111,
+        0.385504,
+        0.157923,
+        0.527123,
+        -0.091458,
+        -0.402758,
+        -0.673286,
+        -0.135910,
+        -0.173314,
+        -0.144002,
+        -0.002444,
+        -0.037396,
+        0.014541,
+        0.178817,
+        -0.677665,
+        -0.133939,
+        -0.215629,
+        0.187498,
+        0.097166,
+        -0.313973,
+        0.111544,
+        -0.316111,
+        -0.475101,
+        0.029520,
+        1.030673,
+        -0.115235,
+        -0.373707,
+        0.140436,
+        0.081270,
+        0.473604,
+        0.052319,
+        -0.032969,
+        0.726690,
+        -0.508942,
+        -0.081261,
+        -0.100095,
+        -0.264550,
+        0.083748,
+        -0.481366,
+        0.374004,
+        0.115747,
+        0.056342,
+        0.381248,
+        0.250238,
+        -0.024464,
+        -0.044359,
+        -0.222250,
+        -0.035241,
+        0.360092,
+        -0.247324,
+        -0.623714,
+        -0.146833,
+        0.408413,
+        -0.062241,
+        0.256813,
+        -0.177144,
+        -0.203659,
+        -0.449947,
+        -0.282204,
+        0.026889,
+        0.415745,
+        0.373556,
+        0.209152,
+        0.126313,
+        0.081538,
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+        -0.212294,
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+        0.199968,
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+        0.127815,
+        -0.653291,
+        -0.217597,
+        -0.207234,
+        -0.137746,
+        -0.572086,
+        -0.166099,
+        -0.056433,
+        0.443503,
+        -0.009743,
+        -0.219962,
+        -0.361587,
+        0.010081,
+        -0.350795,
+        0.159290,
+        -0.154969,
+        0.068987,
+        -0.295963,
+        -0.061066,
+        0.141129,
+        0.088867,
+        0.000831,
+        -0.045378,
+        0.125143,
+        0.214289,
+        -0.296127,
+        0.284706,
+        0.108625,
+        0.177400,
+        0.314614,
+        -0.446847,
+        -0.109241,
+        -0.278223,
+        -0.300109,
+        0.039706,
+        -0.063601,
+        -0.401392,
+        -0.323813,
+        -0.115349,
+        -0.552075,
+        0.332108,
+        -0.008345,
+        -0.229026,
+        0.213410,
+        -0.018742,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_1_modules_1_modules_mlp_modules_0_parameters_weight_:
+    name = "L_self_modules_features_modules_1_modules_1_modules_mlp_modules_0_parameters_weight_"
+    shape = [512, 128]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.001
+    std = 0.047
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_1_modules_1_modules_mlp_modules_0_parameters_bias_:
+    name = "L_self_modules_features_modules_1_modules_1_modules_mlp_modules_0_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.532
+    std = 0.364
+    data = [
+        -0.679722,
+        -0.208796,
+        -0.817291,
+        -0.998958,
+        -0.484154,
+        -0.752778,
+        -0.016698,
+        -0.686745,
+        -0.608089,
+        -0.982537,
+        -0.542882,
+        -1.231126,
+        -0.378895,
+        -0.925610,
+        -1.093054,
+        -0.509736,
+        -0.859424,
+        -0.549849,
+        -0.339976,
+        -0.785581,
+        -0.591882,
+        -0.404672,
+        -1.011076,
+        -0.891124,
+        -0.009834,
+        0.013691,
+        0.021913,
+        -0.479263,
+        -0.011235,
+        -0.416079,
+        -0.362385,
+        -0.481969,
+        0.000534,
+        -0.389293,
+        -0.002449,
+        -0.008746,
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+        -1.064429,
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+        -0.430802,
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+        -0.012698,
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+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_1_modules_1_modules_mlp_modules_3_parameters_weight_:
+    name = "L_self_modules_features_modules_1_modules_1_modules_mlp_modules_3_parameters_weight_"
+    shape = [128, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.049
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_1_modules_1_modules_mlp_modules_3_parameters_bias_:
+    name = "L_self_modules_features_modules_1_modules_1_modules_mlp_modules_3_parameters_bias_"
+    shape = [128]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.004
+    std = 0.294
+    data = [
+        0.222080,
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+        0.086722,
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+        0.253563,
+        0.216009,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_2_modules_norm_parameters_weight_:
+    name = "L_self_modules_features_modules_2_modules_norm_parameters_weight_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.733
+    std = 0.236
+    data = [
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+        0.341856,
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+        0.581033,
+        0.585013,
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+        1.130555,
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+        1.326389,
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+        0.477060,
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+        0.773912,
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+        0.887992,
+        0.871500,
+        0.758335,
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+        0.461995,
+        0.532339,
+        0.651961,
+        1.020806,
+        0.945691,
+        0.790372,
+        0.917428,
+        0.961738,
+        0.778518,
+        0.742887,
+        0.360807,
+        0.591315,
+        0.520004,
+        0.601972,
+        0.787188,
+        0.529893,
+        0.896665,
+        0.555320,
+        0.393022,
+        0.749143,
+        0.590264,
+        0.710128,
+        0.798867,
+        1.012764,
+        0.577750,
+        0.492440,
+        0.985471,
+        0.592343,
+        0.534603,
+        0.699115,
+        0.609211,
+        1.530381,
+        0.713644,
+        0.760897,
+        0.565465,
+        0.618324,
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+        0.951049,
+        0.717508,
+        0.488345,
+        1.002499,
+        0.797426,
+        0.515060,
+        0.753321,
+        0.785403,
+        0.764003,
+        0.492492,
+        0.793672,
+        0.619051,
+        0.742926,
+        0.774491,
+        0.603736,
+        1.544766,
+        0.892196,
+        1.093882,
+        0.993280,
+        0.821394,
+        0.842348,
+        0.364512,
+        0.678203,
+        0.694800,
+        0.928791,
+        0.731158,
+        0.656192,
+        1.129662,
+        0.621026,
+        0.828794,
+        0.903107,
+        0.990482,
+        1.343699,
+        0.624470,
+        0.815534,
+        0.854796,
+        0.767915,
+        0.354759,
+        0.740758,
+        0.815059,
+        0.570490,
+        0.583818,
+        0.419105,
+        1.131020,
+        0.681248,
+        0.976732,
+        0.547711,
+        1.063506,
+        0.635742,
+        0.921993,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_2_modules_norm_parameters_bias_:
+    name = "L_self_modules_features_modules_2_modules_norm_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.030
+    std = 0.246
+    data = [
+        -0.362983,
+        -0.020631,
+        0.137905,
+        0.071717,
+        0.337369,
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+        -0.032252,
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+        1.001913,
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+        0.176060,
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+        0.006011,
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+        0.114998,
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+        0.024177,
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+        -0.156591,
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+        0.120419,
+        -0.084811,
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+        0.142174,
+        -0.119676,
+        0.041826,
+        -0.290467,
+        -0.155348,
+        -0.094331,
+        -0.099574,
+        -0.131087,
+        -0.099209,
+        -0.710849,
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+        0.008726,
+        0.036121,
+        -0.042659,
+        -0.156979,
+        0.068146,
+        0.365535,
+        0.010104,
+        -0.012943,
+        0.121255,
+        0.129968,
+        0.294590,
+        0.028044,
+        0.041089,
+        0.035862,
+        -0.154884,
+        -0.120315,
+        -0.596169,
+        0.071378,
+        -0.037849,
+        -0.299110,
+        -0.054699,
+        -0.385566,
+        0.039770,
+        -0.178113,
+        0.041412,
+        -0.327599,
+        -0.123306,
+        -0.085114,
+        -0.218493,
+        -0.011794,
+        -0.105455,
+        -0.343522,
+        -0.220236,
+        -0.116505,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_2_modules_reduction_parameters_weight_:
+    name = "L_self_modules_features_modules_2_modules_reduction_parameters_weight_"
+    shape = [256, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.043
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_3_modules_0_modules_norm1_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_3_modules_0_modules_norm1_parameters_weight_"
+    )
+    shape = [256]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.567
+    std = 0.142
+    data = [
+        0.680578,
+        0.628731,
+        0.270499,
+        0.694889,
+        0.291236,
+        0.687238,
+        0.517860,
+        0.630019,
+        0.438245,
+        0.600020,
+        0.463570,
+        0.591272,
+        0.443949,
+        0.623607,
+        0.559090,
+        0.736789,
+        0.607181,
+        0.636350,
+        0.642198,
+        0.671073,
+        0.591766,
+        0.740727,
+        0.585967,
+        0.639347,
+        0.362993,
+        0.560251,
+        0.638520,
+        0.689490,
+        0.580840,
+        0.621476,
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+        0.604279,
+        0.660155,
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+        0.624338,
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+        0.368127,
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+        0.382778,
+        0.631581,
+        0.147232,
+        0.612757,
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+        0.715094,
+        0.569423,
+        0.568891,
+        0.691145,
+        0.686339,
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+        0.244807,
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+        0.688329,
+        0.800129,
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+        0.635493,
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+        0.617534,
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+        0.572529,
+        0.415895,
+        0.166611,
+        0.634523,
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+        0.716687,
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+        0.656213,
+        0.285378,
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+        0.535358,
+        0.470141,
+        0.318330,
+        0.528514,
+        0.300568,
+        0.337232,
+        0.593623,
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+        0.705356,
+        0.549164,
+        0.712151,
+        0.658515,
+        0.607740,
+        0.609422,
+        0.814752,
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+        0.504144,
+        0.612796,
+        0.682593,
+        0.504283,
+        0.396943,
+        0.327250,
+        0.637678,
+        0.629555,
+        0.528675,
+        0.667171,
+        0.650734,
+        0.315975,
+        0.395273,
+        0.570536,
+        0.642441,
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+        0.183798,
+        0.576008,
+        0.615311,
+        0.584086,
+        0.365128,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_3_modules_0_modules_norm1_parameters_bias_:
+    name = "L_self_modules_features_modules_3_modules_0_modules_norm1_parameters_bias_"
+    shape = [256]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.002
+    std = 0.132
+    data = [
+        0.098131,
+        0.361403,
+        -0.187459,
+        -0.156296,
+        -0.010935,
+        0.284478,
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+        0.057142,
+        0.035398,
+        0.076730,
+        0.069026,
+        -0.221412,
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+        -0.041572,
+        -0.062950,
+        0.298494,
+        -0.055248,
+        -0.040968,
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+        -0.066130,
+        -0.122278,
+        0.358900,
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+        0.079474,
+        0.151392,
+        -0.026695,
+        0.151208,
+        0.061140,
+        -0.012886,
+        -0.151701,
+        0.217723,
+        -0.013183,
+        0.168936,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_3_modules_0_modules_attn_parameters_relative_position_bias_table_:
+    name = "L_self_modules_features_modules_3_modules_0_modules_attn_parameters_relative_position_bias_table_"
+    shape = [169, 8]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.700
+    std = 1.185
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_3_modules_0_modules_attn_buffers_relative_position_index_:
+    name = "L_self_modules_features_modules_3_modules_0_modules_attn_buffers_relative_position_index_"
+    shape = [2401]
+    dtype = "torch.int64"
+    device = "cpu"
+    mean = None
+    std = None
+    min_val = 0
+    max_val = 168
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_3_modules_0_modules_attn_modules_qkv_parameters_weight_:
+    name = "L_self_modules_features_modules_3_modules_0_modules_attn_modules_qkv_parameters_weight_"
+    shape = [768, 256]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.056
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_3_modules_0_modules_attn_modules_proj_parameters_weight_:
+    name = "L_self_modules_features_modules_3_modules_0_modules_attn_modules_proj_parameters_weight_"
+    shape = [256, 256]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.044
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_3_modules_0_modules_attn_modules_qkv_parameters_bias_:
+    name = "L_self_modules_features_modules_3_modules_0_modules_attn_modules_qkv_parameters_bias_"
+    shape = [768]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.014
+    std = 0.395
+    data = [
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+        0.813015,
+        -0.468339,
+        -0.013024,
+        0.557444,
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+        -0.003916,
+        0.009573,
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+        0.024584,
+        0.061919,
+        0.038973,
+        -0.031628,
+        -0.017597,
+        -0.004375,
+        0.005518,
+        0.022811,
+        0.029906,
+        0.007143,
+        0.037656,
+        -0.000345,
+        0.037175,
+        -0.019968,
+        0.006460,
+        -0.062060,
+        -0.079964,
+        0.019850,
+        0.051112,
+        0.014473,
+        -0.040458,
+        -0.012751,
+        -0.062554,
+        0.011169,
+        -0.021624,
+        0.084755,
+        0.020847,
+        -0.013334,
+        0.008517,
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+        0.063927,
+        0.031169,
+        -0.012695,
+        0.035563,
+        -0.024602,
+        -0.002259,
+        0.011246,
+        0.013174,
+        0.002256,
+        0.004139,
+        -0.009575,
+        -0.055406,
+        -0.044274,
+        0.016508,
+        -0.002538,
+        0.028606,
+        -0.023534,
+        0.072725,
+        -0.008322,
+        -0.003412,
+        0.033988,
+        0.004011,
+        -0.016151,
+        0.010957,
+        -0.022952,
+        0.105248,
+        0.013460,
+        0.012571,
+        -0.002064,
+        0.021104,
+        0.002111,
+        -0.006844,
+        -0.021616,
+        -0.008128,
+        -0.041910,
+        0.055110,
+        0.043255,
+        0.022520,
+        -0.052892,
+        0.067861,
+        -0.022776,
+        -0.021269,
+        0.017250,
+        -0.023104,
+        0.016018,
+        -0.027086,
+        -0.113904,
+        0.010242,
+        0.022948,
+        0.014633,
+        -0.010564,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_3_modules_0_modules_attn_modules_proj_parameters_bias_:
+    name = "L_self_modules_features_modules_3_modules_0_modules_attn_modules_proj_parameters_bias_"
+    shape = [256]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.003
+    std = 0.281
+    data = [
+        0.188097,
+        0.486521,
+        -0.392394,
+        -0.099127,
+        0.217016,
+        0.264891,
+        -0.139941,
+        -0.072680,
+        0.356998,
+        0.416426,
+        0.359540,
+        0.139002,
+        0.147853,
+        -0.448698,
+        -0.497250,
+        0.179010,
+        0.107685,
+        0.125168,
+        -0.078073,
+        0.047982,
+        -0.195046,
+        -0.188609,
+        0.425071,
+        0.198842,
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+        -0.037196,
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+        0.196534,
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+        0.056153,
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+        0.078612,
+        0.246540,
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+        0.197370,
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+        0.142738,
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+        0.294112,
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+        0.076060,
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+        -0.044267,
+        0.433634,
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+        0.016602,
+        0.560971,
+        0.350080,
+        -0.051261,
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+        0.098852,
+        -0.047443,
+        -0.223657,
+        0.130763,
+        0.362223,
+        -0.339675,
+        0.453797,
+        -0.210649,
+        -0.131016,
+        -0.097145,
+        0.459236,
+        0.056903,
+        -0.286414,
+        0.288311,
+        -0.153986,
+        -0.167926,
+        -0.260287,
+        -0.080450,
+        0.221208,
+        -0.185483,
+        -0.229930,
+        -0.509772,
+        -0.014880,
+        0.253980,
+        0.212144,
+        0.121306,
+        -0.243704,
+        -0.116764,
+        0.105185,
+        -0.008044,
+        0.615197,
+        -0.157845,
+        -0.138184,
+        -0.034192,
+        -0.168206,
+        -0.040071,
+        0.407313,
+        -0.397590,
+        -0.108679,
+        -0.155167,
+        0.105579,
+        0.130311,
+        -0.131833,
+        0.024696,
+        -0.188310,
+        0.474289,
+        -0.056566,
+        0.114863,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_3_modules_0_modules_norm2_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_3_modules_0_modules_norm2_parameters_weight_"
+    )
+    shape = [256]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 1.097
+    std = 0.343
+    data = [
+        1.516574,
+        1.197429,
+        -0.000274,
+        1.108928,
+        0.272772,
+        1.377169,
+        0.927228,
+        1.342291,
+        1.139947,
+        1.185529,
+        0.846353,
+        1.005607,
+        1.124872,
+        1.189263,
+        0.930813,
+        1.530718,
+        1.389753,
+        1.181624,
+        1.298032,
+        1.197891,
+        1.281309,
+        1.407334,
+        1.189771,
+        1.330111,
+        0.534245,
+        1.077011,
+        1.320150,
+        1.469334,
+        0.950855,
+        1.053998,
+        1.319792,
+        1.386728,
+        1.306424,
+        1.013137,
+        1.264149,
+        1.396748,
+        1.448449,
+        1.247385,
+        1.079701,
+        0.000175,
+        1.379065,
+        1.408642,
+        1.095906,
+        1.319582,
+        1.290823,
+        1.328998,
+        0.923783,
+        1.086687,
+        0.656690,
+        1.216841,
+        1.172599,
+        1.425719,
+        1.184897,
+        1.220174,
+        1.385849,
+        1.367895,
+        0.571449,
+        1.172767,
+        1.207154,
+        1.250783,
+        1.437678,
+        1.086141,
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+        1.389436,
+        0.227898,
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+        0.001715,
+        0.442348,
+        1.244691,
+        1.309172,
+        0.770661,
+        1.346877,
+        1.262504,
+        1.254305,
+        0.928903,
+        1.066379,
+        1.360733,
+        1.071934,
+        1.426496,
+        1.417089,
+        0.965029,
+        1.166225,
+        0.679617,
+        1.191103,
+        0.004276,
+        1.351110,
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+        1.170776,
+        1.003668,
+        1.201768,
+        1.358731,
+        1.419721,
+        1.318402,
+        1.070123,
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+        1.000163,
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+        1.105032,
+        1.403309,
+        1.220680,
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+        1.112564,
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+        1.317779,
+        1.049574,
+        1.017076,
+        1.334203,
+        0.513845,
+        0.792094,
+        0.853388,
+        1.396662,
+        0.155277,
+        1.342645,
+        1.081696,
+        1.224459,
+        0.879792,
+        1.235209,
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+        1.223143,
+        0.115950,
+        1.182974,
+        1.376581,
+        1.226028,
+        1.362869,
+        0.926082,
+        1.066588,
+        1.157426,
+        1.184092,
+        1.040019,
+        1.270988,
+        1.225061,
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+        1.126870,
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+        1.183413,
+        1.238246,
+        1.148578,
+        1.293072,
+        1.143668,
+        1.440547,
+        1.442538,
+        1.386960,
+        1.161306,
+        1.311903,
+        1.013412,
+        1.195916,
+        1.256334,
+        1.190442,
+        1.227169,
+        1.269188,
+        0.499811,
+        0.431717,
+        1.363732,
+        1.158923,
+        1.066040,
+        -0.000059,
+        1.376662,
+        1.415865,
+        1.353974,
+        1.025049,
+        1.393150,
+        1.193957,
+        1.398141,
+        1.275089,
+        1.223559,
+        0.896302,
+        1.345957,
+        1.254807,
+        1.091788,
+        1.220465,
+        1.198908,
+        0.001145,
+        1.327427,
+        1.284616,
+        1.159093,
+        1.279976,
+        1.315269,
+        1.222265,
+        1.150717,
+        -0.000369,
+        1.395650,
+        1.238686,
+        1.255963,
+        1.068249,
+        1.320940,
+        1.294333,
+        0.865900,
+        -0.000095,
+        1.139961,
+        1.379422,
+        1.170627,
+        1.318027,
+        1.276754,
+        1.228530,
+        1.283303,
+        1.181658,
+        1.337850,
+        0.256777,
+        1.252398,
+        1.076789,
+        1.042613,
+        0.251009,
+        1.133572,
+        0.480113,
+        0.588322,
+        1.086016,
+        0.900085,
+        1.486996,
+        1.294953,
+        1.013553,
+        1.280874,
+        1.155140,
+        1.339123,
+        1.075706,
+        1.315159,
+        1.256020,
+        1.396903,
+        1.278468,
+        1.379567,
+        1.067141,
+        0.655192,
+        0.556544,
+        1.127870,
+        1.250435,
+        1.373978,
+        1.264248,
+        1.385783,
+        0.497820,
+        0.785417,
+        1.248274,
+        1.435482,
+        1.224370,
+        1.170768,
+        1.169279,
+        1.145185,
+        0.731536,
+        1.262388,
+        1.135551,
+        0.185044,
+        1.191489,
+        0.858900,
+        1.023087,
+        0.000275,
+        1.344468,
+        1.159604,
+        1.255700,
+        0.417495,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_3_modules_0_modules_norm2_parameters_bias_:
+    name = "L_self_modules_features_modules_3_modules_0_modules_norm2_parameters_bias_"
+    shape = [256]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.012
+    std = 0.170
+    data = [
+        -0.023531,
+        0.116298,
+        0.000211,
+        -0.145566,
+        -0.081746,
+        0.204216,
+        0.171675,
+        0.066559,
+        0.010792,
+        0.163727,
+        -0.370304,
+        -0.148197,
+        -0.179150,
+        0.074289,
+        0.163629,
+        -0.113589,
+        0.053669,
+        0.205090,
+        -0.131522,
+        0.025158,
+        0.095826,
+        0.324475,
+        -0.064069,
+        0.036213,
+        0.001672,
+        0.344108,
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+        0.029092,
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+        0.096695,
+        0.153831,
+        0.127068,
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+        -0.033770,
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+        -0.000000,
+        0.084262,
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+        0.155084,
+        0.172753,
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+        -0.093916,
+        0.120059,
+        0.279408,
+        -0.185535,
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+        0.066310,
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+        0.118014,
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+        0.031530,
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+        -0.273529,
+        0.001172,
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+        0.013608,
+        -0.111924,
+        -0.315625,
+        -0.105848,
+        0.075413,
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+        -0.001963,
+        -0.200615,
+        0.150435,
+        0.150111,
+        0.242863,
+        0.035914,
+        -0.116077,
+        -0.051302,
+        -0.000440,
+        0.166714,
+        -0.195848,
+        -0.012866,
+        -0.054695,
+        -0.116996,
+        -0.117130,
+        -0.232276,
+        -0.042368,
+        0.065923,
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+        0.088156,
+        -0.086151,
+        -0.109177,
+        -0.021610,
+        0.216049,
+        0.231723,
+        -0.068921,
+        -0.095551,
+        -0.192114,
+        -0.055437,
+        0.115206,
+        0.136053,
+        -0.056509,
+        0.453690,
+        -0.027076,
+        0.113843,
+        0.139986,
+        0.054692,
+        0.120609,
+        -0.065129,
+        0.160931,
+        0.158258,
+        -0.410941,
+        -0.108270,
+        -0.230426,
+        -0.117604,
+        -0.003452,
+        -0.112924,
+        0.151903,
+        -0.120707,
+        0.008927,
+        -0.102059,
+        0.076047,
+        0.079018,
+        -0.024591,
+        0.034326,
+        -0.164896,
+        0.245893,
+        0.125997,
+        0.238165,
+        0.000654,
+        -0.158139,
+        0.150635,
+        0.187456,
+        -0.001796,
+        -0.064793,
+        -0.097349,
+        0.048181,
+        0.203962,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_3_modules_0_modules_mlp_modules_0_parameters_weight_:
+    name = "L_self_modules_features_modules_3_modules_0_modules_mlp_modules_0_parameters_weight_"
+    shape = [1024, 256]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.001
+    std = 0.049
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_3_modules_0_modules_mlp_modules_0_parameters_bias_:
+    name = "L_self_modules_features_modules_3_modules_0_modules_mlp_modules_0_parameters_bias_"
+    shape = [1024]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.727
+    std = 0.282
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_3_modules_0_modules_mlp_modules_3_parameters_weight_:
+    name = "L_self_modules_features_modules_3_modules_0_modules_mlp_modules_3_parameters_weight_"
+    shape = [256, 1024]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.048
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_3_modules_0_modules_mlp_modules_3_parameters_bias_:
+    name = "L_self_modules_features_modules_3_modules_0_modules_mlp_modules_3_parameters_bias_"
+    shape = [256]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.005
+    std = 0.350
+    data = [
+        -0.159777,
+        -0.071958,
+        0.136773,
+        -0.134341,
+        -0.374773,
+        0.165367,
+        0.715930,
+        0.165515,
+        0.049494,
+        0.561652,
+        -0.538175,
+        0.243937,
+        -0.232012,
+        -0.485472,
+        0.634606,
+        -0.317890,
+        0.150492,
+        -0.331394,
+        -0.161203,
+        -0.160796,
+        -0.059458,
+        -0.087720,
+        -0.187233,
+        -0.035130,
+        -0.078792,
+        -0.014206,
+        0.065358,
+        -0.168955,
+        -0.072051,
+        1.453755,
+        0.233143,
+        -0.177114,
+        0.048508,
+        -0.197513,
+        0.382417,
+        0.003645,
+        -0.040888,
+        0.132203,
+        -0.182059,
+        -0.131489,
+        0.067636,
+        0.021946,
+        0.114487,
+        0.138664,
+        -0.030694,
+        0.071534,
+        0.109598,
+        -0.460973,
+        0.608821,
+        0.120606,
+        0.378982,
+        0.008914,
+        0.033548,
+        0.390370,
+        -0.227102,
+        -0.161553,
+        -1.981749,
+        -0.067890,
+        -0.229159,
+        0.112297,
+        0.248507,
+        0.074882,
+        -0.378575,
+        0.309228,
+        -0.034996,
+        -0.195103,
+        -0.268539,
+        0.116658,
+        -0.016309,
+        -0.477353,
+        -0.284373,
+        -0.339129,
+        0.029366,
+        0.051237,
+        -0.549393,
+        -0.146204,
+        0.061425,
+        0.078113,
+        0.138582,
+        -0.169551,
+        -0.023502,
+        0.314266,
+        -0.156221,
+        0.128628,
+        -0.047981,
+        -0.194866,
+        0.226316,
+        0.024575,
+        0.061219,
+        0.024128,
+        -0.209062,
+        -0.043165,
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+        0.255338,
+        0.316555,
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+        -0.035785,
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+        -0.734025,
+        -0.053573,
+        0.096486,
+        0.638212,
+        -0.140199,
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+        -0.044317,
+        0.018718,
+        -0.028144,
+        -0.476652,
+        0.296340,
+        -0.414053,
+        -0.165804,
+        0.175901,
+        0.772188,
+        -0.314784,
+        1.893500,
+        -0.243604,
+        0.203683,
+        0.050779,
+        -0.019764,
+        0.336012,
+        0.084010,
+        0.083728,
+        -0.016918,
+        -0.348456,
+        0.161027,
+        -0.328526,
+        -0.094251,
+        -0.351304,
+        0.313050,
+        -0.130555,
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+        0.050616,
+        0.111150,
+        -0.108134,
+        -0.077908,
+        0.178903,
+        -0.023026,
+        2.001464,
+        0.005155,
+        0.044670,
+        0.268971,
+        -0.441698,
+        0.574264,
+        -0.011166,
+        0.029011,
+        0.177347,
+        -0.174136,
+        -0.172550,
+        0.269557,
+        0.176416,
+        -0.385120,
+        -0.014122,
+        0.151143,
+        0.215816,
+        -0.212001,
+        -0.518609,
+        0.189223,
+        -0.120216,
+        0.004126,
+        -0.083233,
+        0.155334,
+        -0.031482,
+        0.205934,
+        0.782428,
+        -0.096844,
+        -0.083877,
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+        0.054182,
+        0.024704,
+        0.087770,
+        0.267095,
+        -0.051708,
+        -0.122078,
+        -0.399561,
+        0.107420,
+        -0.165089,
+        0.281958,
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+        0.029835,
+        -0.114202,
+        0.369027,
+        -0.018978,
+        -0.032585,
+        -0.012378,
+        0.488649,
+        -0.150111,
+        0.089133,
+        -0.013071,
+        -0.192770,
+        -0.024070,
+        0.251281,
+        0.373066,
+        -0.158590,
+        0.223585,
+        -0.024380,
+        -0.111784,
+        -0.176385,
+        -0.063694,
+        -0.891970,
+        -0.092816,
+        -0.086885,
+        0.015987,
+        0.250349,
+        -0.285795,
+        -0.268119,
+        0.898106,
+        0.733484,
+        0.065019,
+        -0.166092,
+        -0.184995,
+        -0.016205,
+        0.245742,
+        -0.216473,
+        -0.307803,
+        0.189096,
+        0.351847,
+        0.121771,
+        0.035208,
+        0.113244,
+        0.351107,
+        0.136762,
+        -0.099740,
+        0.120545,
+        -0.644816,
+        -0.420921,
+        -0.298669,
+        -0.396099,
+        0.063032,
+        -0.088969,
+        0.486141,
+        -0.451644,
+        0.226654,
+        -0.000585,
+        -0.105379,
+        0.193014,
+        -0.243416,
+        -0.190744,
+        -0.036223,
+        0.034242,
+        0.004527,
+        0.329753,
+        -0.257253,
+        -0.042047,
+        0.062359,
+        0.189529,
+        -0.095669,
+        0.145298,
+        -0.402276,
+        0.172895,
+        -0.084008,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_3_modules_1_modules_norm1_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_3_modules_1_modules_norm1_parameters_weight_"
+    )
+    shape = [256]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.826
+    std = 0.117
+    data = [
+        0.812945,
+        0.865242,
+        0.657149,
+        0.977260,
+        0.467015,
+        0.889836,
+        0.943182,
+        0.758095,
+        0.790065,
+        0.977332,
+        0.868457,
+        0.786819,
+        0.620342,
+        0.782224,
+        0.934162,
+        0.853088,
+        0.845379,
+        0.795955,
+        0.965802,
+        0.873275,
+        0.811793,
+        0.870386,
+        0.865718,
+        0.944344,
+        0.843016,
+        0.740198,
+        0.942577,
+        0.919540,
+        1.062475,
+        0.533923,
+        0.767100,
+        0.767200,
+        0.975439,
+        0.696980,
+        0.766959,
+        0.795711,
+        0.920755,
+        0.914044,
+        0.906677,
+        0.577693,
+        0.806483,
+        0.936594,
+        0.722039,
+        0.819298,
+        0.852918,
+        0.903652,
+        1.165296,
+        0.646291,
+        0.665960,
+        0.759567,
+        0.928868,
+        0.820034,
+        0.930561,
+        0.919212,
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+        0.881820,
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+        0.774932,
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+        0.952561,
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+        0.817268,
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+        0.699132,
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+        0.635099,
+        0.769413,
+        0.707244,
+        1.016881,
+        0.947711,
+        0.904943,
+        0.878470,
+        0.882816,
+        0.775478,
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+        0.925146,
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+        0.804137,
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+        0.770901,
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+        0.791386,
+        0.737183,
+        0.904813,
+        0.705259,
+        0.780642,
+        0.840651,
+        0.840189,
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+        0.697988,
+        0.895271,
+        1.015208,
+        0.686304,
+        0.659468,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_3_modules_1_modules_norm1_parameters_bias_:
+    name = "L_self_modules_features_modules_3_modules_1_modules_norm1_parameters_bias_"
+    shape = [256]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.004
+    std = 0.255
+    data = [
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+        -0.079324,
+        0.030859,
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+        0.101874,
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+        0.031773,
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+        -0.131297,
+        -0.034468,
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+        -0.027431,
+        0.116684,
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+        0.054005,
+        0.143411,
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+        0.257206,
+        0.065576,
+        0.053223,
+        0.417524,
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+        0.064234,
+        0.106430,
+        -0.125902,
+        -0.229971,
+        0.121299,
+        -0.098921,
+        0.284023,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_3_modules_1_modules_attn_parameters_relative_position_bias_table_:
+    name = "L_self_modules_features_modules_3_modules_1_modules_attn_parameters_relative_position_bias_table_"
+    shape = [169, 8]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.780
+    std = 1.177
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_3_modules_1_modules_attn_buffers_relative_position_index_:
+    name = "L_self_modules_features_modules_3_modules_1_modules_attn_buffers_relative_position_index_"
+    shape = [2401]
+    dtype = "torch.int64"
+    device = "cpu"
+    mean = None
+    std = None
+    min_val = 0
+    max_val = 168
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_3_modules_1_modules_attn_modules_qkv_parameters_weight_:
+    name = "L_self_modules_features_modules_3_modules_1_modules_attn_modules_qkv_parameters_weight_"
+    shape = [768, 256]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.055
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_3_modules_1_modules_attn_modules_proj_parameters_weight_:
+    name = "L_self_modules_features_modules_3_modules_1_modules_attn_modules_proj_parameters_weight_"
+    shape = [256, 256]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.046
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_3_modules_1_modules_attn_modules_qkv_parameters_bias_:
+    name = "L_self_modules_features_modules_3_modules_1_modules_attn_modules_qkv_parameters_bias_"
+    shape = [768]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.007
+    std = 0.363
+    data = [
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+        0.670220,
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+        -0.896385,
+        -1.046657,
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+        0.002769,
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+        0.029591,
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+        0.001812,
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+        0.018681,
+        0.010291,
+        -0.011094,
+        0.012657,
+        -0.025991,
+        -0.019317,
+        0.085299,
+        0.033461,
+        -0.038041,
+        -0.025677,
+        0.017430,
+        -0.008425,
+        0.010084,
+        -0.146757,
+        -0.011042,
+        0.065422,
+        -0.136351,
+        -0.119540,
+        -0.130253,
+        -0.002269,
+        0.130893,
+        -0.124871,
+        0.173895,
+        0.093824,
+        -0.215906,
+        -0.264667,
+        -0.078260,
+        0.083504,
+        0.060383,
+        -0.294930,
+        0.022223,
+        0.037813,
+        0.011992,
+        -0.031703,
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+        -0.003022,
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+        0.042428,
+        -0.012758,
+        0.062604,
+        -0.223173,
+        -0.041598,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_3_modules_1_modules_attn_modules_proj_parameters_bias_:
+    name = "L_self_modules_features_modules_3_modules_1_modules_attn_modules_proj_parameters_bias_"
+    shape = [256]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.008
+    std = 0.323
+    data = [
+        0.024927,
+        0.073823,
+        -0.006311,
+        0.046134,
+        -0.348553,
+        -0.082886,
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+        -0.540927,
+        0.111302,
+        0.030473,
+        0.037326,
+        0.393516,
+        0.142595,
+        -0.407920,
+        0.017694,
+        -0.262496,
+        0.180022,
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+        0.203162,
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+        0.053197,
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+        0.442350,
+        0.191521,
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+        0.069935,
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+        0.302907,
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+        0.012978,
+        -0.372732,
+        0.122259,
+        0.196340,
+        -0.001965,
+        0.251666,
+        -0.052444,
+        0.054384,
+        0.116558,
+        0.866225,
+        0.140077,
+        0.335584,
+        -0.274391,
+        0.081039,
+        -0.352788,
+        -0.188181,
+        0.395634,
+        0.436219,
+        0.182518,
+        0.095002,
+        -0.549704,
+        0.037251,
+        -0.554856,
+        -0.438906,
+        0.251538,
+        -0.226116,
+        0.074022,
+        -0.358357,
+        -0.392884,
+        0.204071,
+        0.095381,
+        -0.260782,
+        0.241689,
+        -0.461896,
+        0.042308,
+        0.208561,
+        0.036162,
+        0.033951,
+        -0.247271,
+        0.159600,
+        -0.157027,
+        -0.006166,
+        -0.284221,
+        -0.170865,
+        0.326800,
+        -0.185775,
+        -0.255716,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_3_modules_1_modules_norm2_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_3_modules_1_modules_norm2_parameters_weight_"
+    )
+    shape = [256]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 1.187
+    std = 0.251
+    data = [
+        1.200194,
+        1.399639,
+        0.354299,
+        1.310701,
+        0.586797,
+        1.360811,
+        1.453778,
+        1.125064,
+        1.207342,
+        1.451114,
+        1.244534,
+        1.006399,
+        1.029958,
+        1.201896,
+        1.367805,
+        1.221547,
+        1.366385,
+        1.228658,
+        1.269354,
+        1.227640,
+        1.281183,
+        1.227018,
+        1.206567,
+        1.329963,
+        0.978619,
+        1.140830,
+        1.356527,
+        1.310629,
+        1.549450,
+        1.017455,
+        1.344344,
+        1.198049,
+        1.298507,
+        1.090154,
+        1.251830,
+        1.337477,
+        1.234470,
+        1.342215,
+        1.348621,
+        0.000736,
+        1.301288,
+        1.409461,
+        1.018416,
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+        1.078120,
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+        1.096012,
+        1.106699,
+        1.181413,
+        1.211535,
+        0.903778,
+        0.745330,
+        1.231809,
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+        0.454803,
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+        1.179613,
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+        1.180547,
+        1.332914,
+        1.221986,
+        1.335515,
+        1.283339,
+        1.239931,
+        1.076642,
+        1.262124,
+        1.354423,
+        1.213306,
+        1.399873,
+        1.333080,
+        0.450464,
+        1.086877,
+        1.394608,
+        1.476853,
+        1.280982,
+        1.262257,
+        1.161428,
+        1.405296,
+        0.422522,
+        1.250796,
+        1.363774,
+        1.362873,
+        1.112579,
+        1.227965,
+        1.169851,
+        0.875135,
+        -0.003529,
+        1.202911,
+        1.276405,
+        1.068274,
+        1.316140,
+        1.323165,
+        1.314035,
+        1.331373,
+        1.032245,
+        1.311024,
+        0.665975,
+        1.262509,
+        1.028972,
+        1.155773,
+        0.627670,
+        1.588017,
+        0.860979,
+        0.948267,
+        1.080505,
+        1.261439,
+        1.435794,
+        1.395414,
+        1.264169,
+        1.424579,
+        1.374657,
+        1.418453,
+        1.236625,
+        1.257009,
+        1.415894,
+        1.037977,
+        1.139135,
+        1.296286,
+        1.242959,
+        0.983969,
+        1.002075,
+        1.118224,
+        1.197585,
+        1.229459,
+        1.381404,
+        1.355608,
+        0.799303,
+        1.188174,
+        1.320536,
+        1.372119,
+        1.256500,
+        1.316134,
+        1.176733,
+        1.347387,
+        1.063662,
+        1.261805,
+        1.282445,
+        0.442667,
+        1.290703,
+        1.101549,
+        1.427790,
+        0.440357,
+        1.369195,
+        1.313855,
+        0.989482,
+        0.740902,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_3_modules_1_modules_norm2_parameters_bias_:
+    name = "L_self_modules_features_modules_3_modules_1_modules_norm2_parameters_bias_"
+    shape = [256]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.005
+    std = 0.281
+    data = [
+        0.157426,
+        0.094087,
+        -0.044214,
+        -0.063505,
+        -0.066269,
+        0.182264,
+        0.111763,
+        0.216597,
+        -0.152504,
+        -0.211596,
+        -0.383328,
+        -0.568486,
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+        -0.107367,
+        0.339299,
+        -0.121937,
+        0.444270,
+        -0.141860,
+        0.441326,
+        0.221437,
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+        0.101002,
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+        0.095598,
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+        -0.198678,
+        0.214303,
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+        0.276553,
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+        0.082630,
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+        0.331278,
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+        0.070341,
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+        0.034688,
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+        0.051876,
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+        0.100747,
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+        -0.040503,
+        0.129173,
+        0.245066,
+        -0.255070,
+        -0.025903,
+        -0.137722,
+        -0.017119,
+        -0.104124,
+        -0.294207,
+        0.121878,
+        0.116564,
+        -0.046688,
+        -0.035076,
+        0.381509,
+        0.103752,
+        -0.348775,
+        -0.443156,
+        0.099304,
+        0.120310,
+        -0.473432,
+        -0.212777,
+        0.305710,
+        0.386163,
+        -0.196221,
+        0.219480,
+        -0.226323,
+        0.133276,
+        0.195198,
+        -0.222728,
+        0.114791,
+        0.209222,
+        -0.146892,
+        0.512733,
+        -0.186863,
+        0.065949,
+        0.035925,
+        0.121421,
+        0.186339,
+        -0.388383,
+        0.287270,
+        0.230779,
+        -0.133726,
+        -0.090741,
+        -0.183288,
+        -0.027769,
+        0.571186,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_3_modules_1_modules_mlp_modules_0_parameters_weight_:
+    name = "L_self_modules_features_modules_3_modules_1_modules_mlp_modules_0_parameters_weight_"
+    shape = [1024, 256]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.050
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_3_modules_1_modules_mlp_modules_0_parameters_bias_:
+    name = "L_self_modules_features_modules_3_modules_1_modules_mlp_modules_0_parameters_bias_"
+    shape = [1024]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.660
+    std = 0.202
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_3_modules_1_modules_mlp_modules_3_parameters_weight_:
+    name = "L_self_modules_features_modules_3_modules_1_modules_mlp_modules_3_parameters_weight_"
+    shape = [256, 1024]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.054
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_3_modules_1_modules_mlp_modules_3_parameters_bias_:
+    name = "L_self_modules_features_modules_3_modules_1_modules_mlp_modules_3_parameters_bias_"
+    shape = [256]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.021
+    std = 0.423
+    data = [
+        -0.088625,
+        0.053116,
+        0.126410,
+        -0.204622,
+        -0.220675,
+        0.121299,
+        0.606756,
+        0.232792,
+        0.235097,
+        0.286276,
+        -0.209072,
+        0.000830,
+        -0.278122,
+        0.152897,
+        0.380075,
+        -0.016790,
+        0.480847,
+        0.049818,
+        -0.376675,
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+        0.026343,
+        -0.115055,
+        -0.027883,
+        -0.208345,
+        0.169296,
+        -0.112600,
+        -0.278852,
+        0.126576,
+        2.074010,
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+        0.077807,
+        -0.030573,
+        0.091653,
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+        0.542186,
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+        0.103176,
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+        0.209413,
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+        0.123469,
+        -0.041424,
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+        -0.036231,
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+        -0.161906,
+        -0.697778,
+        -0.288503,
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+        0.098759,
+        -0.382402,
+        -0.317537,
+        0.046785,
+        0.339152,
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+        -0.307093,
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+        0.228249,
+        0.010348,
+        0.241193,
+        0.044588,
+        -0.132070,
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+        0.073056,
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+        0.140576,
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+        -0.192716,
+        0.193605,
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+        0.283586,
+        0.066070,
+        0.036232,
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+        0.135926,
+        0.086611,
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+        0.141353,
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+        0.187036,
+        0.156103,
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+        0.133833,
+        0.138226,
+        0.141672,
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+        0.099779,
+        0.065175,
+        0.368573,
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+        -0.166687,
+        0.316617,
+        0.058346,
+        0.147984,
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+        0.232001,
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+        0.157657,
+        0.041500,
+        -0.018372,
+        0.151709,
+        -0.203997,
+        0.185677,
+        -0.000367,
+        0.125723,
+        -0.053894,
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+        0.176327,
+        -0.157682,
+        -0.191224,
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+        0.291725,
+        0.018523,
+        -0.111951,
+        0.233693,
+        -0.007711,
+        -0.081661,
+        0.160616,
+        0.299384,
+        -0.267464,
+        -0.139654,
+        0.384361,
+        -0.428057,
+        -0.376956,
+        -0.030149,
+        -0.711346,
+        -0.037734,
+        -0.239205,
+        -0.122675,
+        0.173201,
+        -0.351068,
+        -0.201963,
+        0.492201,
+        0.343602,
+        0.407267,
+        -0.062615,
+        -0.138954,
+        -0.059095,
+        0.379049,
+        0.239561,
+        -0.087154,
+        0.170607,
+        0.174781,
+        0.174272,
+        0.061542,
+        -0.135703,
+        0.238602,
+        0.019147,
+        0.079696,
+        0.262965,
+        -0.363617,
+        -0.070881,
+        -0.414400,
+        -0.197276,
+        -0.087498,
+        -0.195460,
+        0.333361,
+        -0.365273,
+        -0.081270,
+        -0.077255,
+        -0.150513,
+        -0.013374,
+        0.055321,
+        0.014358,
+        -0.038663,
+        0.340095,
+        0.111772,
+        0.209708,
+        0.014237,
+        0.190110,
+        0.275403,
+        -0.103493,
+        -0.104202,
+        0.415986,
+        -0.242191,
+        0.272497,
+        0.011355,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_4_modules_norm_parameters_weight_:
+    name = "L_self_modules_features_modules_4_modules_norm_parameters_weight_"
+    shape = [1024]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.389
+    std = 0.101
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_4_modules_norm_parameters_bias_:
+    name = "L_self_modules_features_modules_4_modules_norm_parameters_bias_"
+    shape = [1024]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.002
+    std = 0.174
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_4_modules_reduction_parameters_weight_:
+    name = "L_self_modules_features_modules_4_modules_reduction_parameters_weight_"
+    shape = [512, 1024]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.043
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_0_modules_norm1_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_5_modules_0_modules_norm1_parameters_weight_"
+    )
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.364
+    std = 0.237
+    data = [
+        0.257761,
+        0.525198,
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+        0.771020,
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+        0.147002,
+        0.601857,
+        0.310988,
+        0.595131,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_0_modules_norm1_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_0_modules_norm1_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.004
+    std = 0.078
+    data = [
+        -0.046974,
+        0.070216,
+        0.082525,
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+        0.069101,
+        0.003945,
+        0.076384,
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+        0.000465,
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+        -0.045363,
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+        0.024977,
+        0.001090,
+        0.024209,
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+        0.019007,
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+        -0.046140,
+        -0.047831,
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+        -0.100830,
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+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_0_modules_attn_parameters_relative_position_bias_table_:
+    name = "L_self_modules_features_modules_5_modules_0_modules_attn_parameters_relative_position_bias_table_"
+    shape = [169, 16]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.790
+    std = 1.974
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_0_modules_attn_buffers_relative_position_index_:
+    name = "L_self_modules_features_modules_5_modules_0_modules_attn_buffers_relative_position_index_"
+    shape = [2401]
+    dtype = "torch.int64"
+    device = "cpu"
+    mean = None
+    std = None
+    min_val = 0
+    max_val = 168
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_0_modules_attn_modules_qkv_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_0_modules_attn_modules_qkv_parameters_weight_"
+    shape = [1536, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.049
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_0_modules_attn_modules_proj_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_0_modules_attn_modules_proj_parameters_weight_"
+    shape = [512, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.042
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_0_modules_attn_modules_qkv_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_0_modules_attn_modules_qkv_parameters_bias_"
+    shape = [1536]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.022
+    std = 0.336
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_0_modules_attn_modules_proj_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_0_modules_attn_modules_proj_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.002
+    std = 0.305
+    data = [
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+        0.309913,
+        0.173514,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_0_modules_norm2_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_5_modules_0_modules_norm2_parameters_weight_"
+    )
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.555
+    std = 0.304
+    data = [
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+        0.733690,
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+        1.034349,
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+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_0_modules_norm2_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_0_modules_norm2_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.004
+    std = 0.157
+    data = [
+        0.084469,
+        -0.282658,
+        0.019162,
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+        0.038591,
+        -0.120681,
+        -0.000509,
+        -0.060131,
+        0.189106,
+        -0.168818,
+        -0.156378,
+        0.090172,
+        -0.026770,
+        0.000858,
+        -0.205331,
+        -0.039262,
+        0.003597,
+        -0.147542,
+        -0.276466,
+        -0.058538,
+        -0.339539,
+        0.075377,
+        0.064101,
+        0.049803,
+        0.011604,
+        0.021922,
+        -0.031506,
+        0.052712,
+        -0.000036,
+        0.127185,
+        -0.101553,
+        0.013610,
+        -0.028068,
+        0.001537,
+        0.029392,
+        -0.041480,
+        0.176917,
+        0.032167,
+        0.000912,
+        -0.181596,
+        -0.240811,
+        0.147907,
+        -0.249135,
+        0.042626,
+        0.104585,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_0_modules_mlp_modules_0_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_0_modules_mlp_modules_0_parameters_weight_"
+    shape = [2048, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.001
+    std = 0.047
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_0_modules_mlp_modules_0_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_0_modules_mlp_modules_0_parameters_bias_"
+    shape = [2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.790
+    std = 0.321
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_0_modules_mlp_modules_3_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_0_modules_mlp_modules_3_parameters_weight_"
+    shape = [512, 2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.040
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_0_modules_mlp_modules_3_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_0_modules_mlp_modules_3_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.002
+    std = 0.282
+    data = [
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+        0.136098,
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+        -0.388568,
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+        -0.049348,
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+        0.161529,
+        -0.353773,
+        0.035995,
+        -0.332231,
+        0.433448,
+        0.055734,
+        0.100895,
+        0.037417,
+        -0.168394,
+        0.391772,
+        -0.347870,
+        0.158551,
+        -0.399794,
+        -0.132577,
+        -0.207550,
+        0.044774,
+        -0.308613,
+        0.293914,
+        0.264241,
+        0.103597,
+        -0.010130,
+        0.060042,
+        -0.245975,
+        0.200968,
+        0.108667,
+        0.560230,
+        0.182095,
+        0.078054,
+        0.105846,
+        -0.043097,
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+        0.282325,
+        0.212149,
+        0.281811,
+        0.026009,
+        -0.343786,
+        -0.375800,
+        0.296600,
+        -0.308221,
+        -0.032883,
+        -0.004984,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_1_modules_norm1_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_5_modules_1_modules_norm1_parameters_weight_"
+    )
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.381
+    std = 0.157
+    data = [
+        0.268549,
+        0.541736,
+        0.500706,
+        0.564166,
+        0.486975,
+        0.358270,
+        0.475886,
+        0.398111,
+        0.571644,
+        0.409801,
+        0.004793,
+        0.561283,
+        0.402768,
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+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_1_modules_norm1_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_1_modules_norm1_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.001
+    std = 0.085
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+        -0.076527,
+        0.107732,
+        -0.022376,
+        0.123661,
+        0.001809,
+        -0.005047,
+        -0.084031,
+        0.046477,
+        0.050640,
+        -0.102399,
+        -0.076758,
+        0.037621,
+        0.015074,
+        -0.096415,
+        -0.082048,
+        -0.048795,
+        0.099805,
+        -0.022072,
+        -0.140385,
+        -0.043833,
+        -0.001297,
+        -0.006044,
+        0.013082,
+        -0.038177,
+        0.001035,
+        0.027803,
+        -0.030546,
+        0.005624,
+        -0.087949,
+        -0.279983,
+        -0.097616,
+        0.007255,
+        0.024365,
+        -0.106732,
+        0.013326,
+        -0.014040,
+        0.075675,
+        -0.200598,
+        -0.079181,
+        -0.005467,
+        -0.057054,
+        -0.031223,
+        -0.074973,
+        0.017318,
+        0.109181,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_1_modules_attn_parameters_relative_position_bias_table_:
+    name = "L_self_modules_features_modules_5_modules_1_modules_attn_parameters_relative_position_bias_table_"
+    shape = [169, 16]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.660
+    std = 1.811
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_1_modules_attn_buffers_relative_position_index_:
+    name = "L_self_modules_features_modules_5_modules_1_modules_attn_buffers_relative_position_index_"
+    shape = [2401]
+    dtype = "torch.int64"
+    device = "cpu"
+    mean = None
+    std = None
+    min_val = 0
+    max_val = 168
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_1_modules_attn_modules_qkv_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_1_modules_attn_modules_qkv_parameters_weight_"
+    shape = [1536, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.055
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_1_modules_attn_modules_proj_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_1_modules_attn_modules_proj_parameters_weight_"
+    shape = [512, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.040
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_1_modules_attn_modules_qkv_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_1_modules_attn_modules_qkv_parameters_bias_"
+    shape = [1536]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.013
+    std = 0.341
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_1_modules_attn_modules_proj_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_1_modules_attn_modules_proj_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.004
+    std = 0.208
+    data = [
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+        0.150409,
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+        0.246981,
+        0.113448,
+        0.282066,
+        -0.014030,
+        0.067412,
+        0.003371,
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+        -0.125498,
+        0.120753,
+        0.012899,
+        0.116896,
+        0.133328,
+        -0.160631,
+        0.115879,
+        0.087168,
+        -0.095542,
+        0.020076,
+        0.152092,
+        -0.132256,
+        -0.158555,
+        -0.125376,
+        0.081893,
+        0.112888,
+        0.035735,
+        0.036779,
+        -0.183719,
+        -0.153431,
+        0.067146,
+        0.016653,
+        -0.122055,
+        -0.017042,
+        -0.041846,
+        0.003873,
+        0.009320,
+        0.131236,
+        -0.099713,
+        -0.169811,
+        0.111309,
+        -0.246909,
+        0.234998,
+        0.039380,
+        0.269386,
+        -0.288115,
+        0.099548,
+        0.084806,
+        0.034415,
+        0.189191,
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+        0.200365,
+        -0.236995,
+        -0.019653,
+        0.201795,
+        -0.012159,
+        -0.348341,
+        -0.239679,
+        0.087505,
+        0.168020,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_1_modules_norm2_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_5_modules_1_modules_norm2_parameters_weight_"
+    )
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.628
+    std = 0.220
+    data = [
+        0.237415,
+        0.800500,
+        0.692518,
+        0.859793,
+        0.817612,
+        0.780915,
+        0.857713,
+        0.483411,
+        0.580321,
+        0.768105,
+        0.407499,
+        0.886242,
+        0.805324,
+        0.576593,
+        0.859191,
+        0.767152,
+        0.737443,
+        0.701861,
+        0.775557,
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+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_1_modules_norm2_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_1_modules_norm2_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.003
+    std = 0.183
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+        -0.085213,
+        0.119905,
+        0.000372,
+        -0.329282,
+        0.045929,
+        -0.123977,
+        -0.078807,
+        0.140748,
+        0.206167,
+        -0.157576,
+        -0.174142,
+        0.064675,
+        -0.003100,
+        0.019355,
+        -0.079475,
+        -0.088673,
+        0.042306,
+        0.000333,
+        -0.277636,
+        -0.032559,
+        -0.154876,
+        -0.018155,
+        -0.047813,
+        0.016378,
+        0.072568,
+        0.044958,
+        0.059044,
+        -0.047926,
+        -0.167023,
+        0.434273,
+        0.002651,
+        -0.075066,
+        0.013857,
+        0.001018,
+        -0.087862,
+        0.000160,
+        0.133593,
+        -0.046389,
+        -0.081633,
+        -0.146437,
+        -0.111532,
+        0.219415,
+        -0.036294,
+        -0.040924,
+        0.087721,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_1_modules_mlp_modules_0_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_1_modules_mlp_modules_0_parameters_weight_"
+    shape = [2048, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.048
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_1_modules_mlp_modules_0_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_1_modules_mlp_modules_0_parameters_bias_"
+    shape = [2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.779
+    std = 0.306
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_1_modules_mlp_modules_3_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_1_modules_mlp_modules_3_parameters_weight_"
+    shape = [512, 2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.042
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_1_modules_mlp_modules_3_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_1_modules_mlp_modules_3_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.001
+    std = 0.231
+    data = [
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+        -0.173146,
+        -0.143086,
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+        0.041643,
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+        -0.152873,
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+        0.101200,
+        -0.031141,
+        -0.122751,
+        -0.195783,
+        -0.401080,
+        0.084186,
+        -0.161200,
+        -0.141789,
+        0.327606,
+        -0.068104,
+        -0.036910,
+        -0.088118,
+        0.252351,
+        0.075032,
+        -0.111478,
+        0.006257,
+        0.008488,
+        0.579629,
+        -0.062919,
+        0.230545,
+        -0.391297,
+        0.072413,
+        -0.374778,
+        0.119546,
+        -0.085536,
+        0.315526,
+        0.057600,
+        0.178009,
+        0.165881,
+        0.124760,
+        -0.152607,
+        -0.073993,
+        0.136910,
+        0.429836,
+        0.172515,
+        -0.031381,
+        0.385373,
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+        0.137744,
+        0.096201,
+        -0.016565,
+        0.042455,
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+        -0.225305,
+        0.218545,
+        -0.314085,
+        0.016314,
+        0.159023,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_2_modules_norm1_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_5_modules_2_modules_norm1_parameters_weight_"
+    )
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.520
+    std = 0.196
+    data = [
+        0.318630,
+        0.649105,
+        0.667039,
+        0.502713,
+        0.489055,
+        0.375647,
+        0.619776,
+        0.642102,
+        0.675751,
+        0.514151,
+        0.001065,
+        0.445872,
+        0.599394,
+        0.016072,
+        0.534138,
+        0.393432,
+        0.245430,
+        0.709408,
+        0.704575,
+        0.200436,
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+        0.526505,
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+        0.247347,
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+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_2_modules_norm1_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_2_modules_norm1_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.002
+    std = 0.104
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+        -0.040011,
+        -0.093847,
+        0.011128,
+        0.131196,
+        -0.035338,
+        0.120849,
+        0.014565,
+        -0.036125,
+        -0.183571,
+        0.119775,
+        0.077269,
+        -0.134646,
+        -0.057472,
+        0.011269,
+        0.015063,
+        -0.179527,
+        -0.038870,
+        -0.205505,
+        0.213332,
+        0.042127,
+        -0.116954,
+        -0.002663,
+        0.006673,
+        -0.076344,
+        -0.004421,
+        -0.003295,
+        0.084122,
+        0.004830,
+        0.050156,
+        -0.049034,
+        -0.161684,
+        -0.211931,
+        -0.016468,
+        -0.026063,
+        -0.060736,
+        -0.148983,
+        -0.077088,
+        -0.053896,
+        0.055755,
+        -0.139923,
+        -0.157343,
+        0.007292,
+        -0.025504,
+        -0.019546,
+        0.042602,
+        -0.021874,
+        0.080884,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_2_modules_attn_parameters_relative_position_bias_table_:
+    name = "L_self_modules_features_modules_5_modules_2_modules_attn_parameters_relative_position_bias_table_"
+    shape = [169, 16]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.317
+    std = 1.304
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_2_modules_attn_buffers_relative_position_index_:
+    name = "L_self_modules_features_modules_5_modules_2_modules_attn_buffers_relative_position_index_"
+    shape = [2401]
+    dtype = "torch.int64"
+    device = "cpu"
+    mean = None
+    std = None
+    min_val = 0
+    max_val = 168
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_2_modules_attn_modules_qkv_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_2_modules_attn_modules_qkv_parameters_weight_"
+    shape = [1536, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.051
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_2_modules_attn_modules_proj_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_2_modules_attn_modules_proj_parameters_weight_"
+    shape = [512, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.043
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_2_modules_attn_modules_qkv_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_2_modules_attn_modules_qkv_parameters_bias_"
+    shape = [1536]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.012
+    std = 0.334
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_2_modules_attn_modules_proj_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_2_modules_attn_modules_proj_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.001
+    std = 0.277
+    data = [
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+        0.043995,
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+        0.423190,
+        0.045899,
+        -0.078733,
+        0.388443,
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+        -0.185817,
+        0.001507,
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+        0.233543,
+        0.185840,
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+        0.090916,
+        0.111818,
+        0.026143,
+        -0.005829,
+        -0.338548,
+        0.025471,
+        0.060585,
+        -0.314451,
+        -0.061926,
+        -0.122324,
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+        0.068901,
+        -0.230363,
+        0.004241,
+        -0.273299,
+        -0.043308,
+        -0.044323,
+        0.277780,
+        0.034265,
+        -0.150132,
+        -0.010526,
+        -0.254405,
+        0.035749,
+        0.277762,
+        -0.110973,
+        0.067695,
+        0.027539,
+        0.100585,
+        -0.311486,
+        -0.070705,
+        0.290152,
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+        -0.173535,
+        -0.108834,
+        0.104577,
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+        0.096688,
+        -0.138506,
+        -0.374576,
+        0.322469,
+        0.090331,
+        -0.479691,
+        0.067315,
+        0.030734,
+        0.363427,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_2_modules_norm2_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_5_modules_2_modules_norm2_parameters_weight_"
+    )
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.674
+    std = 0.144
+    data = [
+        0.394605,
+        0.785295,
+        0.732317,
+        0.666704,
+        0.757744,
+        0.759165,
+        0.748348,
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+        0.671189,
+        0.800829,
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+        0.675820,
+        0.717264,
+        0.839224,
+        0.704021,
+        0.741238,
+        0.774362,
+        0.003294,
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+        0.652393,
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+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_2_modules_norm2_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_2_modules_norm2_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.001
+    std = 0.193
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+        0.022307,
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+        -0.180294,
+        -0.085413,
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+        0.043196,
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+        0.174874,
+        -0.000281,
+        -0.323628,
+        0.023426,
+        -0.056057,
+        -0.053448,
+        0.302047,
+        0.166946,
+        0.041288,
+        -0.089167,
+        0.111875,
+        0.025083,
+        0.081996,
+        -0.046485,
+        -0.136256,
+        -0.055496,
+        -0.009143,
+        -0.127525,
+        0.007232,
+        -0.032343,
+        -0.053172,
+        -0.183619,
+        0.072319,
+        0.054425,
+        0.030187,
+        0.031287,
+        0.063969,
+        -0.202600,
+        0.391598,
+        0.085234,
+        -0.160510,
+        0.092742,
+        0.012267,
+        -0.134137,
+        0.024335,
+        0.089863,
+        -0.038017,
+        -0.045141,
+        -0.217429,
+        -0.077723,
+        0.244265,
+        -0.045548,
+        -0.046407,
+        -0.032237,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_2_modules_mlp_modules_0_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_2_modules_mlp_modules_0_parameters_weight_"
+    shape = [2048, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.048
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_2_modules_mlp_modules_0_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_2_modules_mlp_modules_0_parameters_bias_"
+    shape = [2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.717
+    std = 0.217
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_2_modules_mlp_modules_3_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_2_modules_mlp_modules_3_parameters_weight_"
+    shape = [512, 2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.043
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_2_modules_mlp_modules_3_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_2_modules_mlp_modules_3_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.001
+    std = 0.231
+    data = [
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+        -0.132686,
+        0.026615,
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+        -0.216097,
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+        0.214915,
+        0.059142,
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+        0.056390,
+        0.306742,
+        0.137296,
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+        -0.211481,
+        0.013856,
+        0.565056,
+        0.124963,
+        0.171794,
+        -0.420660,
+        -0.029836,
+        -0.436174,
+        0.028151,
+        -0.112121,
+        0.235619,
+        -0.152910,
+        0.225668,
+        0.236999,
+        -0.006059,
+        -0.039123,
+        -0.180983,
+        0.063648,
+        0.254462,
+        0.196210,
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+        -0.130994,
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+        -0.184069,
+        0.213990,
+        -0.246456,
+        0.015200,
+        0.399612,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_3_modules_norm1_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_5_modules_3_modules_norm1_parameters_weight_"
+    )
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.491
+    std = 0.183
+    data = [
+        0.807049,
+        0.540314,
+        0.543860,
+        0.444703,
+        0.451506,
+        0.297322,
+        0.567352,
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+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_3_modules_norm1_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_3_modules_norm1_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.002
+    std = 0.102
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+        -0.033640,
+        0.005026,
+        -0.108573,
+        -0.007328,
+        0.075044,
+        -0.002571,
+        0.186943,
+        -0.010595,
+        -0.017011,
+        -0.060277,
+        0.082791,
+        0.108737,
+        0.013416,
+        -0.046668,
+        0.055739,
+        -0.004918,
+        -0.189978,
+        -0.029536,
+        -0.173612,
+        0.076629,
+        0.026894,
+        -0.003944,
+        -0.015127,
+        0.039539,
+        -0.106503,
+        -0.076251,
+        0.063138,
+        0.060173,
+        0.064487,
+        -0.014361,
+        0.061066,
+        -0.216226,
+        -0.256617,
+        -0.007808,
+        -0.071953,
+        0.033468,
+        -0.208167,
+        -0.075236,
+        -0.071234,
+        0.046480,
+        -0.078573,
+        -0.060213,
+        -0.010151,
+        -0.008647,
+        0.020805,
+        0.008054,
+        -0.047886,
+        -0.013160,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_3_modules_attn_parameters_relative_position_bias_table_:
+    name = "L_self_modules_features_modules_5_modules_3_modules_attn_parameters_relative_position_bias_table_"
+    shape = [169, 16]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.368
+    std = 1.344
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_3_modules_attn_buffers_relative_position_index_:
+    name = "L_self_modules_features_modules_5_modules_3_modules_attn_buffers_relative_position_index_"
+    shape = [2401]
+    dtype = "torch.int64"
+    device = "cpu"
+    mean = None
+    std = None
+    min_val = 0
+    max_val = 168
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_3_modules_attn_modules_qkv_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_3_modules_attn_modules_qkv_parameters_weight_"
+    shape = [1536, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.054
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_3_modules_attn_modules_proj_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_3_modules_attn_modules_proj_parameters_weight_"
+    shape = [512, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.040
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_3_modules_attn_modules_qkv_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_3_modules_attn_modules_qkv_parameters_bias_"
+    shape = [1536]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.006
+    std = 0.320
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_3_modules_attn_modules_proj_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_3_modules_attn_modules_proj_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.001
+    std = 0.218
+    data = [
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+        0.376815,
+        0.173388,
+        -0.389435,
+        0.394179,
+        0.050874,
+        0.070724,
+        -0.049337,
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+        0.089821,
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+        0.120231,
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+        -0.109776,
+        0.017971,
+        0.105389,
+        -0.145729,
+        0.088405,
+        0.009122,
+        -0.154901,
+        0.040346,
+        0.045526,
+        -0.161511,
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+        -0.026383,
+        -0.058587,
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+        -0.108462,
+        -0.127184,
+        0.031603,
+        0.082525,
+        -0.142808,
+        0.109080,
+        -0.039824,
+        -0.258622,
+        0.362813,
+        -0.030725,
+        0.057650,
+        0.262215,
+        -0.267796,
+        0.091222,
+        -0.067569,
+        0.140242,
+        -0.005860,
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+        -0.114144,
+        0.035788,
+        0.082619,
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+        0.256967,
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+        -0.085292,
+        0.232856,
+        -0.050307,
+        -0.164088,
+        -0.178464,
+        -0.018180,
+        0.211594,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_3_modules_norm2_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_5_modules_3_modules_norm2_parameters_weight_"
+    )
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.720
+    std = 0.133
+    data = [
+        0.672140,
+        0.775638,
+        0.794504,
+        0.738131,
+        0.782595,
+        0.798188,
+        0.805847,
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+        0.919092,
+        0.789195,
+        0.726379,
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+        0.009902,
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+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_3_modules_norm2_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_3_modules_norm2_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.004
+    std = 0.202
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+        0.061138,
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+        0.047336,
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+        0.036636,
+        -0.175717,
+        0.101071,
+        0.000521,
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+        0.002360,
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+        -0.023347,
+        0.165790,
+        -0.002774,
+        -0.359569,
+        -0.021962,
+        -0.031859,
+        -0.094227,
+        0.324319,
+        0.139848,
+        0.115419,
+        -0.042691,
+        0.106109,
+        0.039113,
+        0.063043,
+        0.026567,
+        -0.137215,
+        -0.013457,
+        -0.078967,
+        -0.025031,
+        -0.059444,
+        0.065288,
+        0.013533,
+        -0.327740,
+        0.104411,
+        -0.004130,
+        -0.010493,
+        0.084423,
+        0.056779,
+        -0.255584,
+        0.293041,
+        0.049899,
+        -0.093433,
+        0.137262,
+        -0.044215,
+        -0.165745,
+        0.047994,
+        0.107898,
+        -0.058616,
+        -0.081052,
+        -0.238955,
+        -0.016993,
+        0.209293,
+        0.058196,
+        -0.067062,
+        -0.070751,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_3_modules_mlp_modules_0_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_3_modules_mlp_modules_0_parameters_weight_"
+    shape = [2048, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.048
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_3_modules_mlp_modules_0_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_3_modules_mlp_modules_0_parameters_bias_"
+    shape = [2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.705
+    std = 0.206
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_3_modules_mlp_modules_3_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_3_modules_mlp_modules_3_parameters_weight_"
+    shape = [512, 2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.045
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_3_modules_mlp_modules_3_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_3_modules_mlp_modules_3_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.001
+    std = 0.189
+    data = [
+        -0.184119,
+        -0.097134,
+        0.070820,
+        0.018004,
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+        0.139211,
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+        0.219064,
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+        -0.207703,
+        0.016498,
+        0.061220,
+        -0.429620,
+        -0.145090,
+        0.208438,
+        -0.211943,
+        0.048909,
+        0.139014,
+        -0.013522,
+        -0.192368,
+        0.146288,
+        0.222911,
+        0.169935,
+        -0.136638,
+        -0.139273,
+        -0.029179,
+        0.362983,
+        0.140634,
+        0.138604,
+        -0.371052,
+        0.034459,
+        -0.364952,
+        0.124719,
+        -0.003806,
+        0.152652,
+        -0.209704,
+        0.068266,
+        0.282978,
+        0.018005,
+        0.018502,
+        -0.098104,
+        0.029622,
+        0.193019,
+        0.009646,
+        -0.067379,
+        0.370358,
+        -0.128841,
+        -0.056347,
+        0.099006,
+        0.125259,
+        -0.281102,
+        -0.366286,
+        -0.488677,
+        -0.313027,
+        0.135289,
+        -0.135829,
+        -0.153560,
+        0.429565,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_4_modules_norm1_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_5_modules_4_modules_norm1_parameters_weight_"
+    )
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.563
+    std = 0.223
+    data = [
+        0.548745,
+        0.602843,
+        0.733927,
+        0.471946,
+        0.504619,
+        0.270637,
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+        0.860256,
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+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_4_modules_norm1_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_4_modules_norm1_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.003
+    std = 0.113
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+        0.286386,
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+        0.025361,
+        0.040607,
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+        0.037293,
+        0.004557,
+        0.023012,
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+        -0.088578,
+        0.072023,
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+        0.077078,
+        0.074404,
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+        0.224826,
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+        -0.069181,
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+        0.095762,
+        -0.016326,
+        -0.037028,
+        0.029261,
+        -0.098149,
+        0.032363,
+        0.070654,
+        -0.029599,
+        0.186700,
+        -0.030670,
+        0.022218,
+        -0.066175,
+        0.093023,
+        0.088464,
+        0.098026,
+        -0.023814,
+        0.049285,
+        0.027090,
+        -0.113348,
+        0.009504,
+        -0.244351,
+        0.065283,
+        0.029576,
+        0.089513,
+        -0.008169,
+        0.002781,
+        -0.073192,
+        -0.163498,
+        0.114427,
+        0.019029,
+        -0.044169,
+        0.036043,
+        0.021458,
+        -0.184532,
+        -0.335484,
+        -0.048110,
+        -0.054038,
+        0.076583,
+        -0.145312,
+        -0.117402,
+        -0.088378,
+        0.018359,
+        -0.060195,
+        -0.068387,
+        0.000594,
+        0.046426,
+        0.028367,
+        0.052273,
+        -0.068104,
+        -0.041132,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_4_modules_attn_parameters_relative_position_bias_table_:
+    name = "L_self_modules_features_modules_5_modules_4_modules_attn_parameters_relative_position_bias_table_"
+    shape = [169, 16]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.298
+    std = 1.370
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_4_modules_attn_buffers_relative_position_index_:
+    name = "L_self_modules_features_modules_5_modules_4_modules_attn_buffers_relative_position_index_"
+    shape = [2401]
+    dtype = "torch.int64"
+    device = "cpu"
+    mean = None
+    std = None
+    min_val = 0
+    max_val = 168
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_4_modules_attn_modules_qkv_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_4_modules_attn_modules_qkv_parameters_weight_"
+    shape = [1536, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.051
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_4_modules_attn_modules_proj_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_4_modules_attn_modules_proj_parameters_weight_"
+    shape = [512, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.044
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_4_modules_attn_modules_qkv_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_4_modules_attn_modules_qkv_parameters_bias_"
+    shape = [1536]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.006
+    std = 0.325
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_4_modules_attn_modules_proj_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_4_modules_attn_modules_proj_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.001
+    std = 0.245
+    data = [
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+        0.008800,
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+        0.313007,
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+        -0.217654,
+        0.467849,
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+        0.179502,
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+        0.183466,
+        -0.152455,
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+        0.116218,
+        0.004996,
+        0.024416,
+        -0.117185,
+        0.047386,
+        0.264898,
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+        0.115726,
+        -0.368984,
+        -0.324992,
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+        0.061308,
+        -0.265492,
+        -0.058082,
+        -0.140639,
+        0.274607,
+        0.001722,
+        0.004884,
+        -0.048111,
+        -0.064450,
+        -0.142499,
+        0.194039,
+        -0.072203,
+        0.085793,
+        0.085576,
+        0.055619,
+        -0.305518,
+        -0.166087,
+        0.203099,
+        -0.149699,
+        0.111188,
+        -0.112998,
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+        -0.063130,
+        -0.133214,
+        0.174425,
+        -0.151661,
+        -0.313727,
+        0.287649,
+        0.031393,
+        -0.235032,
+        0.068534,
+        0.007292,
+        0.289555,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_4_modules_norm2_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_5_modules_4_modules_norm2_parameters_weight_"
+    )
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.750
+    std = 0.123
+    data = [
+        0.586189,
+        0.805533,
+        0.807252,
+        0.781856,
+        0.823281,
+        0.823103,
+        0.809742,
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+        0.934602,
+        0.821219,
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+        0.347995,
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+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_4_modules_norm2_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_4_modules_norm2_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.005
+    std = 0.198
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+        -0.005569,
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+        0.019900,
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+        0.023012,
+        -0.000157,
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+        0.026315,
+        -0.075452,
+        -0.259533,
+        0.026511,
+        -0.121256,
+        0.027484,
+        -0.092875,
+        -0.004859,
+        0.032546,
+        -0.098762,
+        0.121946,
+        0.033186,
+        -0.403894,
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+        -0.128229,
+        -0.174425,
+        0.060521,
+        -0.130543,
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+        -0.001676,
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+        0.075387,
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+        0.123574,
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+        0.370797,
+        0.067984,
+        -0.490990,
+        -0.037039,
+        0.151585,
+        0.052785,
+        -0.392891,
+        -0.052165,
+        -0.098432,
+        -0.020572,
+        0.385550,
+        0.055145,
+        0.165404,
+        0.052517,
+        0.084556,
+        0.051129,
+        0.064372,
+        0.057403,
+        -0.180159,
+        -0.071502,
+        -0.057857,
+        0.016727,
+        -0.011315,
+        0.015360,
+        0.005380,
+        -0.337984,
+        0.148597,
+        -0.035725,
+        -0.047784,
+        0.067881,
+        0.078704,
+        -0.248257,
+        0.048448,
+        0.092852,
+        -0.104113,
+        0.116062,
+        0.036484,
+        -0.178286,
+        0.103316,
+        0.096836,
+        0.013118,
+        -0.005174,
+        -0.218982,
+        0.023151,
+        0.212454,
+        0.069387,
+        -0.031865,
+        -0.168264,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_4_modules_mlp_modules_0_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_4_modules_mlp_modules_0_parameters_weight_"
+    shape = [2048, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.048
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_4_modules_mlp_modules_0_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_4_modules_mlp_modules_0_parameters_bias_"
+    shape = [2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.682
+    std = 0.184
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_4_modules_mlp_modules_3_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_4_modules_mlp_modules_3_parameters_weight_"
+    shape = [512, 2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.046
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_4_modules_mlp_modules_3_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_4_modules_mlp_modules_3_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.001
+    std = 0.176
+    data = [
+        -0.195787,
+        -0.119332,
+        0.068579,
+        0.057733,
+        -0.211586,
+        0.066548,
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+        0.022012,
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+        0.019650,
+        0.159903,
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+        0.149615,
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+        0.280399,
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+        0.115673,
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+        -0.318432,
+        0.071031,
+        0.069564,
+        -0.493338,
+        -0.094016,
+        0.155607,
+        -0.111592,
+        0.042623,
+        0.101456,
+        -0.007171,
+        -0.166634,
+        0.130481,
+        0.057795,
+        0.190506,
+        -0.097328,
+        -0.131213,
+        -0.089887,
+        0.178005,
+        0.079095,
+        0.118213,
+        -0.170533,
+        -0.010702,
+        -0.305286,
+        0.015110,
+        -0.007218,
+        0.220193,
+        -0.354409,
+        0.001542,
+        0.406853,
+        -0.029538,
+        -0.043494,
+        -0.093705,
+        0.025326,
+        0.195804,
+        0.048107,
+        -0.000288,
+        0.288463,
+        -0.107136,
+        -0.056641,
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+        0.136508,
+        -0.314264,
+        -0.335545,
+        -0.357081,
+        -0.260612,
+        0.141511,
+        -0.050640,
+        -0.094555,
+        0.366670,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_5_modules_norm1_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_5_modules_5_modules_norm1_parameters_weight_"
+    )
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.535
+    std = 0.216
+    data = [
+        1.067704,
+        0.445358,
+        0.607838,
+        0.471341,
+        0.448031,
+        0.283957,
+        0.584075,
+        0.751489,
+        0.851442,
+        0.428331,
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+        0.346352,
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+        0.176150,
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+        0.238725,
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+        0.319218,
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+        0.627550,
+        0.551830,
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+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_5_modules_norm1_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_5_modules_norm1_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.002
+    std = 0.118
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+        0.002999,
+        0.193398,
+        -0.039198,
+        0.007815,
+        0.026540,
+        -0.057439,
+        0.011148,
+        -0.055982,
+        0.016557,
+        -0.003437,
+        -0.048521,
+        0.086951,
+        0.000580,
+        -0.035799,
+        0.210197,
+        -0.233242,
+        0.317995,
+        -0.070531,
+        -0.060460,
+        0.021128,
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+        0.030426,
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+        -0.029779,
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+        0.054059,
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+        -0.052803,
+        0.032340,
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+        -0.005001,
+        0.032963,
+        -0.025378,
+        0.198421,
+        -0.047732,
+        -0.039411,
+        -0.006333,
+        0.086376,
+        0.066632,
+        0.112866,
+        -0.005656,
+        0.059324,
+        -0.004173,
+        -0.082552,
+        0.013218,
+        -0.152478,
+        -0.019104,
+        0.021308,
+        0.091841,
+        0.001278,
+        0.016848,
+        -0.030986,
+        -0.129006,
+        0.078600,
+        -0.004725,
+        -0.001738,
+        -0.000469,
+        0.086265,
+        -0.174181,
+        -0.373051,
+        -0.023357,
+        -0.066276,
+        0.066785,
+        -0.131137,
+        -0.061570,
+        -0.060147,
+        0.016029,
+        -0.012807,
+        -0.031456,
+        -0.015725,
+        0.083231,
+        0.038796,
+        0.038956,
+        -0.089468,
+        -0.052563,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_5_modules_attn_parameters_relative_position_bias_table_:
+    name = "L_self_modules_features_modules_5_modules_5_modules_attn_parameters_relative_position_bias_table_"
+    shape = [169, 16]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.199
+    std = 0.972
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_5_modules_attn_buffers_relative_position_index_:
+    name = "L_self_modules_features_modules_5_modules_5_modules_attn_buffers_relative_position_index_"
+    shape = [2401]
+    dtype = "torch.int64"
+    device = "cpu"
+    mean = None
+    std = None
+    min_val = 0
+    max_val = 168
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_5_modules_attn_modules_qkv_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_5_modules_attn_modules_qkv_parameters_weight_"
+    shape = [1536, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.054
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_5_modules_attn_modules_proj_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_5_modules_attn_modules_proj_parameters_weight_"
+    shape = [512, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.041
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_5_modules_attn_modules_qkv_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_5_modules_attn_modules_qkv_parameters_bias_"
+    shape = [1536]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.006
+    std = 0.325
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_5_modules_attn_modules_proj_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_5_modules_attn_modules_proj_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.001
+    std = 0.191
+    data = [
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+        0.048838,
+        0.001451,
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+        0.052513,
+        -0.052869,
+        0.062657,
+        0.305313,
+        0.116620,
+        -0.278756,
+        0.339322,
+        0.112367,
+        0.230314,
+        -0.005013,
+        0.018231,
+        0.032333,
+        -0.162779,
+        0.153561,
+        0.150380,
+        -0.015894,
+        -0.188458,
+        -0.091790,
+        0.022572,
+        0.017898,
+        -0.073354,
+        0.103790,
+        0.004409,
+        -0.044697,
+        0.060518,
+        0.172593,
+        -0.240031,
+        -0.284580,
+        -0.006730,
+        -0.036715,
+        -0.198967,
+        -0.056716,
+        -0.142618,
+        0.002664,
+        0.027602,
+        0.047196,
+        0.016020,
+        -0.037331,
+        -0.186470,
+        0.240152,
+        -0.023816,
+        0.021012,
+        0.157830,
+        -0.131048,
+        0.033562,
+        -0.065850,
+        0.100476,
+        -0.017982,
+        -0.071709,
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+        0.076985,
+        0.161218,
+        -0.121259,
+        0.206176,
+        -0.178959,
+        -0.156943,
+        0.191124,
+        0.088085,
+        -0.139614,
+        -0.066352,
+        0.040591,
+        0.252240,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_5_modules_norm2_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_5_modules_5_modules_norm2_parameters_weight_"
+    )
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.784
+    std = 0.131
+    data = [
+        0.778525,
+        0.821874,
+        0.752350,
+        0.850620,
+        0.830876,
+        0.876827,
+        0.804920,
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+        0.955693,
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+        0.789566,
+        0.747108,
+        0.400788,
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+        0.746462,
+        0.828482,
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+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_5_modules_norm2_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_5_modules_norm2_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.007
+    std = 0.208
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+        -0.029421,
+        0.023404,
+        -0.100464,
+        -0.243009,
+        -0.008978,
+        -0.000702,
+        0.065573,
+        0.003970,
+        -0.089325,
+        0.034348,
+        -0.096844,
+        -0.287131,
+        -0.030071,
+        -0.129938,
+        0.014678,
+        -0.095158,
+        0.003139,
+        -0.012938,
+        -0.014959,
+        0.159221,
+        0.111653,
+        -0.401723,
+        -0.137653,
+        -0.121241,
+        -0.173188,
+        -0.009508,
+        -0.169597,
+        -0.338902,
+        0.111062,
+        -0.376446,
+        -0.043256,
+        0.090298,
+        -0.035516,
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+        0.020458,
+        0.166766,
+        -0.136370,
+        0.407042,
+        0.040547,
+        -0.460796,
+        -0.023898,
+        0.128722,
+        0.090543,
+        -0.426826,
+        -0.046927,
+        -0.093916,
+        -0.029474,
+        0.545997,
+        0.007860,
+        0.209590,
+        0.097701,
+        0.081185,
+        0.086510,
+        0.043669,
+        0.071061,
+        -0.194702,
+        -0.070009,
+        -0.093250,
+        0.022404,
+        0.004398,
+        0.019140,
+        0.073845,
+        -0.343902,
+        0.108410,
+        -0.067036,
+        -0.048108,
+        0.065770,
+        0.111463,
+        -0.254872,
+        -0.073220,
+        0.083393,
+        -0.050645,
+        0.106469,
+        -0.014489,
+        -0.202901,
+        0.165390,
+        0.137798,
+        0.017715,
+        0.045336,
+        -0.246698,
+        0.040034,
+        0.175437,
+        0.107352,
+        -0.045501,
+        -0.288261,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_5_modules_mlp_modules_0_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_5_modules_mlp_modules_0_parameters_weight_"
+    shape = [2048, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.049
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_5_modules_mlp_modules_0_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_5_modules_mlp_modules_0_parameters_bias_"
+    shape = [2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.670
+    std = 0.169
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_5_modules_mlp_modules_3_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_5_modules_mlp_modules_3_parameters_weight_"
+    shape = [512, 2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.048
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_5_modules_mlp_modules_3_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_5_modules_mlp_modules_3_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.002
+    std = 0.142
+    data = [
+        -0.134179,
+        -0.044468,
+        0.020039,
+        0.060923,
+        -0.088975,
+        0.105859,
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+        -0.047578,
+        -0.202931,
+        -0.251512,
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+        0.001734,
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+        -0.250160,
+        0.104556,
+        -0.012932,
+        -0.414632,
+        -0.089049,
+        0.235331,
+        -0.169068,
+        0.020863,
+        0.036355,
+        -0.028224,
+        -0.085291,
+        0.115780,
+        0.017933,
+        0.160768,
+        0.000568,
+        -0.015128,
+        -0.113436,
+        0.184937,
+        0.041600,
+        0.018957,
+        -0.085103,
+        0.061247,
+        -0.248988,
+        0.102232,
+        0.107339,
+        0.048698,
+        -0.287857,
+        -0.057303,
+        0.299881,
+        -0.045445,
+        0.027905,
+        -0.016262,
+        0.048504,
+        0.211380,
+        -0.066182,
+        -0.064048,
+        0.170849,
+        -0.116445,
+        -0.049693,
+        -0.016792,
+        0.083479,
+        -0.272418,
+        -0.180357,
+        -0.275839,
+        -0.127481,
+        0.114407,
+        0.001824,
+        -0.070618,
+        0.223152,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_6_modules_norm1_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_5_modules_6_modules_norm1_parameters_weight_"
+    )
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.606
+    std = 0.233
+    data = [
+        0.668425,
+        0.503030,
+        0.788086,
+        0.493662,
+        0.528758,
+        0.313296,
+        0.605106,
+        1.010428,
+        0.685666,
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+        0.164942,
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+        0.143520,
+        0.450375,
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+        0.260023,
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+        0.388772,
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+        0.758288,
+        0.578173,
+        0.979096,
+        0.509836,
+        0.652365,
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+        0.270882,
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+        0.764646,
+        0.860854,
+        0.520373,
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+        0.712092,
+        1.125926,
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+        0.772215,
+        0.321699,
+        0.925332,
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+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_6_modules_norm1_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_6_modules_norm1_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.004
+    std = 0.131
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+        -0.118031,
+        -0.062211,
+        -0.059141,
+        -0.094479,
+        -0.106662,
+        0.062946,
+        -0.080488,
+        -0.133254,
+        0.075804,
+        -0.027597,
+        -0.011836,
+        -0.108257,
+        0.046516,
+        -0.037080,
+        -0.058975,
+        0.018859,
+        -0.104336,
+        0.030954,
+        -0.000482,
+        -0.058771,
+        0.233937,
+        -0.054341,
+        -0.036568,
+        -0.042025,
+        0.081437,
+        0.029714,
+        0.268893,
+        0.016297,
+        0.036992,
+        0.022725,
+        -0.048402,
+        0.045859,
+        -0.189632,
+        -0.047647,
+        0.029740,
+        0.141385,
+        -0.013840,
+        0.022337,
+        -0.017950,
+        -0.126318,
+        0.083279,
+        -0.033239,
+        -0.063979,
+        0.003727,
+        0.078855,
+        -0.126258,
+        -0.426993,
+        -0.027861,
+        -0.025952,
+        0.049400,
+        -0.093777,
+        -0.120748,
+        -0.042015,
+        -0.013125,
+        0.019383,
+        -0.014880,
+        0.001060,
+        0.104909,
+        0.037302,
+        0.043158,
+        -0.126781,
+        -0.065463,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_6_modules_attn_parameters_relative_position_bias_table_:
+    name = "L_self_modules_features_modules_5_modules_6_modules_attn_parameters_relative_position_bias_table_"
+    shape = [169, 16]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.109
+    std = 0.917
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_6_modules_attn_buffers_relative_position_index_:
+    name = "L_self_modules_features_modules_5_modules_6_modules_attn_buffers_relative_position_index_"
+    shape = [2401]
+    dtype = "torch.int64"
+    device = "cpu"
+    mean = None
+    std = None
+    min_val = 0
+    max_val = 168
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_6_modules_attn_modules_qkv_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_6_modules_attn_modules_qkv_parameters_weight_"
+    shape = [1536, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.051
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_6_modules_attn_modules_proj_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_6_modules_attn_modules_proj_parameters_weight_"
+    shape = [512, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.045
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_6_modules_attn_modules_qkv_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_6_modules_attn_modules_qkv_parameters_bias_"
+    shape = [1536]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.002
+    std = 0.353
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_6_modules_attn_modules_proj_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_6_modules_attn_modules_proj_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.001
+    std = 0.249
+    data = [
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+        0.205227,
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+        0.386735,
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+        -0.000053,
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+        0.103790,
+        0.192618,
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+        0.243269,
+        0.018123,
+        0.042927,
+        -0.055298,
+        -0.075080,
+        0.116963,
+        -0.071103,
+        0.111305,
+        0.174015,
+        -0.399791,
+        -0.136920,
+        -0.241778,
+        0.119795,
+        -0.258803,
+        -0.071517,
+        -0.142717,
+        0.154261,
+        -0.033201,
+        0.250100,
+        0.091097,
+        -0.022926,
+        -0.177558,
+        0.095180,
+        -0.001244,
+        -0.111789,
+        0.039095,
+        0.165546,
+        -0.181086,
+        -0.222677,
+        0.079352,
+        -0.104500,
+        0.068592,
+        0.018996,
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+        0.034010,
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+        0.087021,
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+        -0.413104,
+        0.154245,
+        0.062853,
+        -0.257719,
+        0.079112,
+        0.061451,
+        0.244263,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_6_modules_norm2_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_5_modules_6_modules_norm2_parameters_weight_"
+    )
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.808
+    std = 0.136
+    data = [
+        0.723691,
+        0.832979,
+        0.794921,
+        0.915202,
+        0.837025,
+        0.887626,
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+        1.072456,
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+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_6_modules_norm2_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_6_modules_norm2_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.008
+    std = 0.206
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+        -0.405081,
+        0.056269,
+        -0.111210,
+        -0.180736,
+        0.028050,
+        -0.156530,
+        -0.282247,
+        0.100078,
+        -0.398884,
+        -0.071747,
+        0.147495,
+        -0.042224,
+        -0.183357,
+        -0.020821,
+        0.194694,
+        -0.126640,
+        0.400719,
+        0.086014,
+        -0.257717,
+        -0.087042,
+        0.089135,
+        0.089334,
+        -0.424505,
+        -0.078414,
+        -0.036606,
+        0.042440,
+        0.533250,
+        -0.047515,
+        0.256382,
+        0.141515,
+        0.109825,
+        0.036499,
+        0.062800,
+        0.116357,
+        -0.179048,
+        -0.094389,
+        -0.066773,
+        0.000014,
+        -0.028689,
+        -0.017407,
+        0.135593,
+        -0.315661,
+        0.100555,
+        -0.102693,
+        -0.031742,
+        0.061892,
+        0.107198,
+        -0.243429,
+        -0.261478,
+        0.086306,
+        -0.042721,
+        0.042264,
+        0.001931,
+        -0.199492,
+        0.223685,
+        0.144903,
+        0.116462,
+        0.157634,
+        -0.201405,
+        0.075466,
+        0.202823,
+        0.063820,
+        -0.033260,
+        -0.382092,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_6_modules_mlp_modules_0_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_6_modules_mlp_modules_0_parameters_weight_"
+    shape = [2048, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.049
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_6_modules_mlp_modules_0_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_6_modules_mlp_modules_0_parameters_bias_"
+    shape = [2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.649
+    std = 0.149
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_6_modules_mlp_modules_3_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_6_modules_mlp_modules_3_parameters_weight_"
+    shape = [512, 2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.050
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_6_modules_mlp_modules_3_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_6_modules_mlp_modules_3_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.003
+    std = 0.142
+    data = [
+        -0.121187,
+        -0.104918,
+        0.059123,
+        0.161478,
+        -0.064621,
+        0.153091,
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+        -0.075191,
+        -0.173736,
+        -0.183496,
+        -0.201412,
+        0.184473,
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+        0.216386,
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+        0.174072,
+        0.011755,
+        -0.431234,
+        -0.049175,
+        0.144536,
+        -0.107130,
+        -0.023073,
+        0.077237,
+        -0.030127,
+        -0.070093,
+        0.104150,
+        -0.051874,
+        0.106867,
+        0.013960,
+        -0.047432,
+        -0.123945,
+        0.151404,
+        0.053178,
+        -0.003957,
+        0.025621,
+        0.033466,
+        -0.256264,
+        0.027779,
+        0.053436,
+        0.093158,
+        -0.270841,
+        -0.023235,
+        0.278373,
+        -0.038295,
+        -0.013802,
+        -0.086535,
+        -0.080525,
+        0.280317,
+        -0.049670,
+        -0.029241,
+        0.106097,
+        -0.054261,
+        -0.225027,
+        -0.037319,
+        0.082851,
+        -0.188834,
+        -0.083526,
+        -0.188633,
+        -0.226533,
+        0.189169,
+        0.038093,
+        0.062319,
+        0.145909,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_7_modules_norm1_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_5_modules_7_modules_norm1_parameters_weight_"
+    )
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.581
+    std = 0.233
+    data = [
+        1.203253,
+        0.451816,
+        0.707380,
+        0.525791,
+        0.487741,
+        0.302901,
+        0.583108,
+        0.860251,
+        0.907386,
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+        0.181276,
+        0.368755,
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+        0.362437,
+        0.265938,
+        0.727803,
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+        0.388933,
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+        0.434759,
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+        0.275412,
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+        0.989883,
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+        0.562929,
+        0.218251,
+        0.318198,
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+        1.002544,
+        1.103325,
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+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_7_modules_norm1_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_7_modules_norm1_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.004
+    std = 0.133
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+        0.000641,
+        0.209865,
+        -0.006677,
+        0.038201,
+        -0.000476,
+        0.003726,
+        0.046243,
+        -0.134454,
+        -0.064549,
+        0.012970,
+        0.110966,
+        -0.047352,
+        0.022092,
+        0.029774,
+        -0.104088,
+        0.056776,
+        0.004196,
+        -0.002927,
+        -0.008191,
+        0.085306,
+        -0.104761,
+        -0.430692,
+        -0.020084,
+        -0.036128,
+        -0.005412,
+        -0.075773,
+        -0.074914,
+        0.013658,
+        -0.015723,
+        0.017523,
+        0.016044,
+        -0.030902,
+        0.115168,
+        0.042030,
+        0.016060,
+        -0.151520,
+        -0.046296,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_7_modules_attn_parameters_relative_position_bias_table_:
+    name = "L_self_modules_features_modules_5_modules_7_modules_attn_parameters_relative_position_bias_table_"
+    shape = [169, 16]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.167
+    std = 0.909
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_7_modules_attn_buffers_relative_position_index_:
+    name = "L_self_modules_features_modules_5_modules_7_modules_attn_buffers_relative_position_index_"
+    shape = [2401]
+    dtype = "torch.int64"
+    device = "cpu"
+    mean = None
+    std = None
+    min_val = 0
+    max_val = 168
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_7_modules_attn_modules_qkv_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_7_modules_attn_modules_qkv_parameters_weight_"
+    shape = [1536, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.052
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_7_modules_attn_modules_proj_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_7_modules_attn_modules_proj_parameters_weight_"
+    shape = [512, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.043
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_7_modules_attn_modules_qkv_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_7_modules_attn_modules_qkv_parameters_bias_"
+    shape = [1536]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.002
+    std = 0.344
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_7_modules_attn_modules_proj_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_7_modules_attn_modules_proj_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.002
+    std = 0.173
+    data = [
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+        0.059308,
+        0.125774,
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+        0.047266,
+        0.031873,
+        0.091381,
+        0.240996,
+        0.036916,
+        -0.213132,
+        0.259468,
+        0.032262,
+        0.146215,
+        0.028669,
+        0.056216,
+        0.032869,
+        -0.140243,
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+        0.127949,
+        0.034296,
+        -0.188190,
+        -0.115638,
+        0.108175,
+        0.078800,
+        -0.096846,
+        0.003859,
+        -0.017740,
+        0.017803,
+        0.194293,
+        0.212530,
+        -0.243081,
+        -0.135121,
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+        -0.213669,
+        0.018982,
+        -0.141367,
+        -0.019989,
+        -0.016241,
+        0.234207,
+        -0.000537,
+        -0.075072,
+        -0.268129,
+        0.079145,
+        -0.021331,
+        -0.018929,
+        0.065222,
+        -0.132198,
+        -0.079058,
+        -0.137387,
+        0.041492,
+        -0.023128,
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+        0.136468,
+        0.260996,
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+        0.185713,
+        -0.120345,
+        -0.132801,
+        0.077675,
+        0.104650,
+        -0.127582,
+        -0.001663,
+        0.028332,
+        0.110403,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_7_modules_norm2_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_5_modules_7_modules_norm2_parameters_weight_"
+    )
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.856
+    std = 0.147
+    data = [
+        0.858174,
+        0.914130,
+        0.837082,
+        1.011612,
+        0.905059,
+        0.952579,
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+        0.944568,
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+        0.794616,
+        0.482422,
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+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_7_modules_norm2_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_7_modules_norm2_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.011
+    std = 0.217
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+        -0.066653,
+        -0.058783,
+        -0.005636,
+        0.599318,
+        -0.086264,
+        0.307485,
+        0.155420,
+        0.108626,
+        0.034155,
+        0.077682,
+        0.108857,
+        -0.209377,
+        -0.114276,
+        -0.080061,
+        -0.049962,
+        -0.034923,
+        -0.063104,
+        0.185110,
+        -0.307693,
+        0.090778,
+        -0.093804,
+        -0.008906,
+        0.085541,
+        0.155225,
+        -0.204338,
+        -0.249194,
+        0.049979,
+        -0.025099,
+        0.058559,
+        0.006047,
+        -0.281629,
+        0.229486,
+        0.083838,
+        0.192492,
+        0.212619,
+        -0.203507,
+        0.079840,
+        0.168854,
+        0.064761,
+        -0.059712,
+        -0.407350,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_7_modules_mlp_modules_0_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_7_modules_mlp_modules_0_parameters_weight_"
+    shape = [2048, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.049
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_7_modules_mlp_modules_0_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_7_modules_mlp_modules_0_parameters_bias_"
+    shape = [2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.640
+    std = 0.140
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_7_modules_mlp_modules_3_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_7_modules_mlp_modules_3_parameters_weight_"
+    shape = [512, 2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.052
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_7_modules_mlp_modules_3_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_7_modules_mlp_modules_3_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.004
+    std = 0.132
+    data = [
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+        -0.084985,
+        -0.049630,
+        0.165340,
+        0.013482,
+        0.100614,
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+        -0.049840,
+        -0.113070,
+        -0.183233,
+        -0.161737,
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+        -0.191821,
+        0.130402,
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+        -0.318312,
+        -0.034979,
+        0.167136,
+        -0.152184,
+        -0.034078,
+        0.052809,
+        -0.071576,
+        0.033069,
+        0.105946,
+        -0.061234,
+        0.182784,
+        0.072267,
+        0.061424,
+        -0.109248,
+        0.158528,
+        0.043550,
+        -0.029484,
+        0.017628,
+        0.023783,
+        -0.191188,
+        0.046509,
+        0.021079,
+        -0.009996,
+        -0.102793,
+        -0.009859,
+        0.214507,
+        -0.059176,
+        0.039849,
+        -0.019885,
+        -0.024864,
+        0.316844,
+        -0.092815,
+        0.006335,
+        0.018231,
+        -0.037024,
+        -0.176751,
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+        0.001447,
+        -0.154570,
+        0.088683,
+        -0.151558,
+        -0.142276,
+        0.092977,
+        0.095371,
+        0.034072,
+        -0.024879,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_8_modules_norm1_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_5_modules_8_modules_norm1_parameters_weight_"
+    )
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.638
+    std = 0.243
+    data = [
+        0.820821,
+        0.495305,
+        0.850541,
+        0.565170,
+        0.529929,
+        0.318430,
+        0.607456,
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+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_8_modules_norm1_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_8_modules_norm1_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.005
+    std = 0.141
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+        -0.081564,
+        -0.146579,
+        -0.024080,
+        -0.043801,
+        0.048470,
+        0.018733,
+        -0.020520,
+        0.136223,
+        0.056929,
+        0.015545,
+        -0.157894,
+        -0.060369,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_8_modules_attn_parameters_relative_position_bias_table_:
+    name = "L_self_modules_features_modules_5_modules_8_modules_attn_parameters_relative_position_bias_table_"
+    shape = [169, 16]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.106
+    std = 1.049
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_8_modules_attn_buffers_relative_position_index_:
+    name = "L_self_modules_features_modules_5_modules_8_modules_attn_buffers_relative_position_index_"
+    shape = [2401]
+    dtype = "torch.int64"
+    device = "cpu"
+    mean = None
+    std = None
+    min_val = 0
+    max_val = 168
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_8_modules_attn_modules_qkv_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_8_modules_attn_modules_qkv_parameters_weight_"
+    shape = [1536, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.051
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_8_modules_attn_modules_proj_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_8_modules_attn_modules_proj_parameters_weight_"
+    shape = [512, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.045
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_8_modules_attn_modules_qkv_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_8_modules_attn_modules_qkv_parameters_bias_"
+    shape = [1536]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.002
+    std = 0.357
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_8_modules_attn_modules_proj_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_8_modules_attn_modules_proj_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.002
+    std = 0.241
+    data = [
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+        0.187175,
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+        0.219387,
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+        0.264816,
+        -0.055975,
+        0.016065,
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+        0.244170,
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+        0.118598,
+        0.013875,
+        0.050718,
+        -0.099683,
+        -0.202392,
+        0.014718,
+        -0.000865,
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+        0.214044,
+        -0.423430,
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+        -0.025637,
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+        0.137090,
+        -0.029261,
+        0.310931,
+        0.108656,
+        -0.033102,
+        -0.110731,
+        -0.002929,
+        -0.140475,
+        -0.103073,
+        0.056905,
+        0.050177,
+        -0.033089,
+        -0.235721,
+        0.195478,
+        -0.131488,
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+        0.055229,
+        -0.040260,
+        -0.098060,
+        0.040474,
+        -0.043913,
+        0.154792,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_8_modules_norm2_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_5_modules_8_modules_norm2_parameters_weight_"
+    )
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.897
+    std = 0.161
+    data = [
+        0.843759,
+        0.969329,
+        0.887671,
+        1.068655,
+        0.930676,
+        0.954560,
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+        0.851980,
+        0.807569,
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+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_8_modules_norm2_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_8_modules_norm2_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.011
+    std = 0.221
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+        -0.172585,
+        -0.465949,
+        0.088884,
+        -0.028028,
+        0.017755,
+        0.008862,
+        -0.263767,
+        0.244641,
+        0.114511,
+        0.256759,
+        0.298874,
+        -0.180088,
+        0.140665,
+        0.161814,
+        0.054042,
+        -0.042297,
+        -0.462554,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_8_modules_mlp_modules_0_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_8_modules_mlp_modules_0_parameters_weight_"
+    shape = [2048, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.049
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_8_modules_mlp_modules_0_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_8_modules_mlp_modules_0_parameters_bias_"
+    shape = [2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.627
+    std = 0.130
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_8_modules_mlp_modules_3_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_8_modules_mlp_modules_3_parameters_weight_"
+    shape = [512, 2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.054
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_8_modules_mlp_modules_3_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_8_modules_mlp_modules_3_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.005
+    std = 0.141
+    data = [
+        -0.039257,
+        -0.054820,
+        -0.070302,
+        0.272960,
+        0.004943,
+        0.049633,
+        -0.238283,
+        -0.034181,
+        -0.059131,
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+        -0.206603,
+        0.201711,
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+        -0.169413,
+        0.120061,
+        0.023162,
+        -0.363966,
+        0.015466,
+        0.084794,
+        -0.107246,
+        0.013569,
+        0.003087,
+        -0.110949,
+        -0.002850,
+        0.064243,
+        -0.114779,
+        0.154473,
+        0.070959,
+        0.002866,
+        -0.016617,
+        0.050731,
+        0.095343,
+        0.041231,
+        0.054810,
+        -0.003082,
+        -0.158128,
+        0.096093,
+        -0.014699,
+        -0.014373,
+        -0.167827,
+        -0.008635,
+        0.148392,
+        -0.063390,
+        0.014403,
+        -0.036666,
+        -0.092046,
+        0.279258,
+        0.021140,
+        -0.028999,
+        0.041631,
+        -0.047518,
+        -0.264959,
+        -0.043991,
+        -0.024680,
+        -0.033844,
+        0.068487,
+        -0.103898,
+        -0.125403,
+        0.086721,
+        0.050376,
+        -0.011135,
+        -0.061723,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_9_modules_norm1_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_5_modules_9_modules_norm1_parameters_weight_"
+    )
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.643
+    std = 0.234
+    data = [
+        1.313417,
+        0.457714,
+        0.788772,
+        0.632280,
+        0.560421,
+        0.379989,
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+        1.138833,
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+        1.109929,
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+        0.686226,
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+        0.680009,
+        0.252263,
+        0.615581,
+        0.664108,
+        0.780100,
+        0.928147,
+        1.025001,
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+        0.863029,
+        0.520311,
+        0.727223,
+        0.694393,
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+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_9_modules_norm1_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_9_modules_norm1_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.009
+    std = 0.142
+    data = [
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+        -0.024140,
+        -0.004396,
+        0.098910,
+        -0.056650,
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+        0.029180,
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+        0.141310,
+        0.071203,
+        -0.012018,
+        -0.176367,
+        -0.027812,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_9_modules_attn_parameters_relative_position_bias_table_:
+    name = "L_self_modules_features_modules_5_modules_9_modules_attn_parameters_relative_position_bias_table_"
+    shape = [169, 16]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.013
+    std = 0.700
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_9_modules_attn_buffers_relative_position_index_:
+    name = "L_self_modules_features_modules_5_modules_9_modules_attn_buffers_relative_position_index_"
+    shape = [2401]
+    dtype = "torch.int64"
+    device = "cpu"
+    mean = None
+    std = None
+    min_val = 0
+    max_val = 168
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_9_modules_attn_modules_qkv_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_9_modules_attn_modules_qkv_parameters_weight_"
+    shape = [1536, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.051
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_9_modules_attn_modules_proj_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_9_modules_attn_modules_proj_parameters_weight_"
+    shape = [512, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.045
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_9_modules_attn_modules_qkv_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_9_modules_attn_modules_qkv_parameters_bias_"
+    shape = [1536]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.010
+    std = 0.385
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_9_modules_attn_modules_proj_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_9_modules_attn_modules_proj_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.002
+    std = 0.182
+    data = [
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+        0.034533,
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+        0.106185,
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+        0.195491,
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+        -0.022590,
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+        0.111618,
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+        0.032973,
+        -0.057366,
+        0.005469,
+        0.056413,
+        0.085438,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_9_modules_norm2_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_5_modules_9_modules_norm2_parameters_weight_"
+    )
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.945
+    std = 0.176
+    data = [
+        0.796900,
+        1.032412,
+        0.916488,
+        1.109476,
+        0.990976,
+        1.030924,
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+        1.222526,
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+        1.067878,
+        0.949841,
+        0.970684,
+        0.752754,
+        1.175286,
+        0.823715,
+        0.823293,
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+        1.281418,
+        0.986286,
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+        0.930639,
+        0.821850,
+        0.967331,
+        0.771243,
+        0.906513,
+        1.043177,
+        0.953877,
+        1.018748,
+        0.845610,
+        0.917010,
+        1.130004,
+        0.997626,
+        0.946053,
+        0.693876,
+        0.781478,
+        0.721815,
+        0.866222,
+        0.818794,
+        1.030696,
+        1.584711,
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+        0.993784,
+        0.756748,
+        0.889614,
+        1.006125,
+        0.696090,
+        1.005411,
+        0.957725,
+        1.072483,
+        1.029675,
+        0.847726,
+        1.029547,
+        0.835047,
+        1.019628,
+        1.068682,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_9_modules_norm2_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_9_modules_norm2_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.012
+    std = 0.225
+    data = [
+        -0.226043,
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+        0.137964,
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+        0.341241,
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+        0.175464,
+        0.178877,
+        0.052703,
+        -0.048692,
+        -0.460016,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_9_modules_mlp_modules_0_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_9_modules_mlp_modules_0_parameters_weight_"
+    shape = [2048, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.049
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_9_modules_mlp_modules_0_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_9_modules_mlp_modules_0_parameters_bias_"
+    shape = [2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.620
+    std = 0.129
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_9_modules_mlp_modules_3_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_9_modules_mlp_modules_3_parameters_weight_"
+    shape = [512, 2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.056
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_9_modules_mlp_modules_3_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_9_modules_mlp_modules_3_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.004
+    std = 0.127
+    data = [
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+        0.030886,
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+        0.022421,
+        0.016827,
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+        0.086784,
+        -0.093227,
+        -0.045821,
+        -0.057360,
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+        0.002141,
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+        -0.148258,
+        0.040393,
+        0.107578,
+        0.008830,
+        -0.197839,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_10_modules_norm1_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_5_modules_10_modules_norm1_parameters_weight_"
+    )
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.693
+    std = 0.244
+    data = [
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+        0.504355,
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+        0.597850,
+        0.380907,
+        1.240052,
+        0.576690,
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+        1.015174,
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+        0.729643,
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+        0.817202,
+        0.813370,
+        0.616333,
+        0.766831,
+        0.681241,
+        0.240562,
+        0.648172,
+        0.666088,
+        1.144980,
+        0.964076,
+        0.991600,
+        0.820769,
+        0.885950,
+        0.540999,
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+        0.537723,
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+        1.016004,
+        1.023463,
+        0.953476,
+        1.258915,
+        0.443034,
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+        0.657078,
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+        0.997815,
+        0.548239,
+        0.764961,
+        0.742257,
+        0.502075,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_10_modules_norm1_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_10_modules_norm1_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.009
+    std = 0.152
+    data = [
+        -0.124279,
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+        -0.049084,
+        -0.136847,
+        0.242242,
+        -0.012774,
+        -0.052271,
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+        0.038112,
+        -0.036105,
+        0.502291,
+        -0.013588,
+        0.022254,
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+        0.067652,
+        0.023557,
+        -0.133322,
+        -0.135141,
+        0.013658,
+        0.128699,
+        -0.056372,
+        0.016629,
+        0.123129,
+        -0.047890,
+        0.074215,
+        -0.024319,
+        -0.067648,
+        -0.003886,
+        0.142445,
+        -0.034098,
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+        -0.027605,
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+        -0.054493,
+        -0.105505,
+        -0.187772,
+        -0.048678,
+        -0.041833,
+        0.053486,
+        0.044155,
+        -0.051516,
+        0.167129,
+        0.075956,
+        0.001660,
+        -0.169806,
+        -0.047871,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_10_modules_attn_parameters_relative_position_bias_table_:
+    name = "L_self_modules_features_modules_5_modules_10_modules_attn_parameters_relative_position_bias_table_"
+    shape = [169, 16]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.040
+    std = 0.625
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_10_modules_attn_buffers_relative_position_index_:
+    name = "L_self_modules_features_modules_5_modules_10_modules_attn_buffers_relative_position_index_"
+    shape = [2401]
+    dtype = "torch.int64"
+    device = "cpu"
+    mean = None
+    std = None
+    min_val = 0
+    max_val = 168
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_10_modules_attn_modules_qkv_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_10_modules_attn_modules_qkv_parameters_weight_"
+    shape = [1536, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.050
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_10_modules_attn_modules_proj_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_10_modules_attn_modules_proj_parameters_weight_"
+    shape = [512, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.047
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_10_modules_attn_modules_qkv_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_10_modules_attn_modules_qkv_parameters_bias_"
+    shape = [1536]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.004
+    std = 0.399
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_10_modules_attn_modules_proj_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_10_modules_attn_modules_proj_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.228
+    data = [
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+        -0.306654,
+        -0.043855,
+        -0.166050,
+        0.055969,
+        0.056089,
+        0.301689,
+        0.129767,
+        -0.124508,
+        -0.121403,
+        0.055887,
+        -0.203808,
+        -0.113156,
+        0.046297,
+        0.108224,
+        0.057914,
+        -0.190159,
+        0.106709,
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+        -0.061607,
+        -0.024781,
+        -0.011424,
+        0.041877,
+        0.066655,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_10_modules_norm2_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_5_modules_10_modules_norm2_parameters_weight_"
+    )
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 1.025
+    std = 0.198
+    data = [
+        0.901041,
+        1.116331,
+        0.985148,
+        1.230460,
+        1.118529,
+        1.143324,
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+        1.452191,
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+        1.057454,
+        1.103493,
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+        0.701997,
+        1.175572,
+        0.823557,
+        1.021420,
+        1.010149,
+        0.337736,
+        0.995914,
+        1.004330,
+        1.090029,
+        1.121593,
+        1.074331,
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+        1.119112,
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+        0.427340,
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+        0.905589,
+        1.054551,
+        1.325457,
+        0.774165,
+        1.145444,
+        1.033843,
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+        1.000605,
+        1.203238,
+        0.896702,
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+        1.525472,
+        1.049497,
+        1.112886,
+        1.045690,
+        1.081425,
+        0.808932,
+        1.270965,
+        0.916425,
+        0.827567,
+        0.995627,
+        1.419439,
+        1.021323,
+        0.833643,
+        1.004396,
+        0.938598,
+        1.035628,
+        0.857857,
+        0.908587,
+        1.134721,
+        1.001791,
+        1.136325,
+        0.966242,
+        1.015796,
+        1.224329,
+        1.061923,
+        1.049891,
+        0.773293,
+        0.900590,
+        0.701239,
+        0.910267,
+        0.972947,
+        1.103083,
+        1.626191,
+        1.052202,
+        1.090928,
+        0.798433,
+        0.904889,
+        1.089245,
+        0.616751,
+        1.111843,
+        1.079800,
+        1.167310,
+        1.102687,
+        0.993285,
+        1.030652,
+        0.952591,
+        1.052936,
+        1.118308,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_10_modules_norm2_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_10_modules_norm2_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.008
+    std = 0.233
+    data = [
+        -0.339610,
+        -0.371284,
+        0.078313,
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+        0.056322,
+        0.207785,
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+        0.005618,
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+        -0.039150,
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+        -0.021421,
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+        0.117128,
+        0.092161,
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+        0.195846,
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+        -0.250755,
+        0.247462,
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+        0.018508,
+        -0.031341,
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+        -0.203992,
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+        0.115750,
+        0.039135,
+        -0.547348,
+        0.282349,
+        -0.072024,
+        -0.258383,
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+        -0.158860,
+        -0.085930,
+        0.271879,
+        -0.606419,
+        -0.244134,
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+        -0.019899,
+        -0.186633,
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+        0.235851,
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+        0.422311,
+        0.180865,
+        -0.037848,
+        -0.158149,
+        -0.073627,
+        0.140778,
+        -0.424991,
+        -0.054637,
+        -0.027317,
+        -0.020773,
+        0.654461,
+        -0.207941,
+        0.458038,
+        0.141581,
+        0.125572,
+        0.169905,
+        0.167450,
+        0.081487,
+        -0.295911,
+        -0.117048,
+        -0.172072,
+        -0.007894,
+        -0.089482,
+        -0.177036,
+        0.420018,
+        -0.258924,
+        0.180643,
+        -0.108418,
+        0.045476,
+        0.107327,
+        0.135743,
+        -0.041920,
+        -0.414631,
+        0.021934,
+        -0.002282,
+        0.065409,
+        -0.037929,
+        -0.363161,
+        0.293637,
+        0.161224,
+        0.361760,
+        0.412232,
+        -0.125837,
+        0.264471,
+        0.211035,
+        0.096429,
+        -0.128901,
+        -0.475617,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_10_modules_mlp_modules_0_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_10_modules_mlp_modules_0_parameters_weight_"
+    shape = [2048, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.049
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_10_modules_mlp_modules_0_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_10_modules_mlp_modules_0_parameters_bias_"
+    shape = [2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.609
+    std = 0.132
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_10_modules_mlp_modules_3_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_10_modules_mlp_modules_3_parameters_weight_"
+    shape = [512, 2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.058
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_10_modules_mlp_modules_3_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_10_modules_mlp_modules_3_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.004
+    std = 0.130
+    data = [
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+        -0.118121,
+        -0.025768,
+        0.068709,
+        0.038834,
+        -0.191865,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_11_modules_norm1_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_5_modules_11_modules_norm1_parameters_weight_"
+    )
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.699
+    std = 0.222
+    data = [
+        1.332219,
+        0.524958,
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+        1.113730,
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+        0.457890,
+        1.155716,
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+        0.551049,
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+        0.696035,
+        0.457449,
+        0.583170,
+        0.810163,
+        1.172995,
+        0.746915,
+        0.740961,
+        0.806931,
+        0.630600,
+        0.698412,
+        0.749150,
+        0.297843,
+        0.657848,
+        0.711041,
+        0.785360,
+        0.973898,
+        1.046584,
+        0.799844,
+        0.717569,
+        0.639281,
+        0.598615,
+        0.541359,
+        0.716835,
+        1.080379,
+        1.396506,
+        0.957380,
+        1.249223,
+        0.535966,
+        0.728774,
+        0.707896,
+        0.713174,
+        0.902475,
+        0.561033,
+        0.724022,
+        0.746738,
+        0.510670,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_11_modules_norm1_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_11_modules_norm1_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.013
+    std = 0.139
+    data = [
+        -0.172462,
+        -0.095886,
+        0.068531,
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+        0.051566,
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+        0.019831,
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+        -0.203410,
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+        -0.122957,
+        0.063166,
+        -0.121827,
+        -0.065327,
+        -0.053846,
+        -0.129882,
+        0.221949,
+        0.006686,
+        -0.016986,
+        -0.078371,
+        0.027512,
+        -0.037848,
+        0.335539,
+        -0.046249,
+        0.002536,
+        0.041899,
+        0.082854,
+        0.019688,
+        -0.103706,
+        -0.090440,
+        -0.043598,
+        0.112100,
+        -0.107395,
+        0.021882,
+        0.149069,
+        -0.061921,
+        0.076924,
+        0.003262,
+        -0.057243,
+        -0.000070,
+        0.087455,
+        -0.021896,
+        -0.316112,
+        -0.029718,
+        -0.052797,
+        -0.096375,
+        -0.050360,
+        -0.175535,
+        0.018388,
+        -0.033600,
+        0.048238,
+        0.037640,
+        -0.091704,
+        0.153857,
+        0.074285,
+        -0.019079,
+        -0.196791,
+        -0.013393,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_11_modules_attn_parameters_relative_position_bias_table_:
+    name = "L_self_modules_features_modules_5_modules_11_modules_attn_parameters_relative_position_bias_table_"
+    shape = [169, 16]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.060
+    std = 0.624
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_11_modules_attn_buffers_relative_position_index_:
+    name = "L_self_modules_features_modules_5_modules_11_modules_attn_buffers_relative_position_index_"
+    shape = [2401]
+    dtype = "torch.int64"
+    device = "cpu"
+    mean = None
+    std = None
+    min_val = 0
+    max_val = 168
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_11_modules_attn_modules_qkv_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_11_modules_attn_modules_qkv_parameters_weight_"
+    shape = [1536, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.051
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_11_modules_attn_modules_proj_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_11_modules_attn_modules_proj_parameters_weight_"
+    shape = [512, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.046
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_11_modules_attn_modules_qkv_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_11_modules_attn_modules_qkv_parameters_bias_"
+    shape = [1536]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.019
+    std = 0.446
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_11_modules_attn_modules_proj_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_11_modules_attn_modules_proj_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.001
+    std = 0.151
+    data = [
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+        0.075418,
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+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_11_modules_norm2_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_5_modules_11_modules_norm2_parameters_weight_"
+    )
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 1.144
+    std = 0.219
+    data = [
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+        1.228076,
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+        1.225645,
+        1.072267,
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+        1.188708,
+        1.187779,
+        1.060820,
+        0.737388,
+        1.262302,
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+        1.176723,
+        1.117785,
+        0.359973,
+        1.122283,
+        1.222557,
+        1.263573,
+        1.188992,
+        1.263621,
+        0.856004,
+        1.279050,
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+        1.146498,
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+        0.940057,
+        1.167067,
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+        1.340603,
+        1.000540,
+        1.155781,
+        1.449030,
+        0.840219,
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+        1.130409,
+        1.293929,
+        0.989482,
+        1.165781,
+        1.628515,
+        1.141796,
+        1.279147,
+        1.178153,
+        1.163250,
+        0.896608,
+        1.466076,
+        0.998868,
+        0.957332,
+        1.103562,
+        1.628886,
+        1.188535,
+        0.958478,
+        1.168053,
+        1.105894,
+        1.155703,
+        0.913023,
+        1.082541,
+        1.200905,
+        1.206213,
+        1.260767,
+        1.064017,
+        1.178633,
+        1.352093,
+        1.245029,
+        1.216271,
+        0.884877,
+        0.980556,
+        0.797766,
+        1.077987,
+        1.018963,
+        1.214960,
+        1.832137,
+        1.225913,
+        1.219089,
+        0.853187,
+        0.942455,
+        1.219554,
+        0.533642,
+        1.206558,
+        1.254770,
+        1.286286,
+        1.195691,
+        1.082200,
+        1.202925,
+        1.074461,
+        1.233536,
+        1.333189,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_11_modules_norm2_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_11_modules_norm2_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.004
+    std = 0.252
+    data = [
+        -0.280169,
+        -0.420437,
+        0.161953,
+        0.566098,
+        0.003526,
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+        0.298019,
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+        0.286081,
+        0.161441,
+        0.022366,
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+        0.270254,
+        -0.068877,
+        -0.286712,
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+        -0.229252,
+        0.020137,
+        0.331028,
+        -0.663427,
+        -0.312501,
+        0.177317,
+        -0.071504,
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+        0.002727,
+        0.260308,
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+        0.410503,
+        0.137783,
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+        -0.156237,
+        -0.102359,
+        0.151736,
+        -0.601520,
+        -0.015501,
+        -0.039181,
+        -0.056178,
+        0.684866,
+        -0.227378,
+        0.560823,
+        0.150086,
+        0.129448,
+        0.296287,
+        0.179596,
+        0.082553,
+        -0.332795,
+        -0.146309,
+        -0.146093,
+        0.012770,
+        -0.161616,
+        -0.246239,
+        0.542262,
+        -0.272362,
+        0.163089,
+        -0.161233,
+        0.089800,
+        0.141414,
+        0.146100,
+        -0.071035,
+        -0.184697,
+        -0.017916,
+        0.015283,
+        0.088911,
+        -0.052688,
+        -0.457292,
+        0.272545,
+        0.097113,
+        0.398599,
+        0.442281,
+        -0.248077,
+        0.282066,
+        0.180648,
+        0.099852,
+        -0.216902,
+        -0.515030,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_11_modules_mlp_modules_0_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_11_modules_mlp_modules_0_parameters_weight_"
+    shape = [2048, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.049
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_11_modules_mlp_modules_0_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_11_modules_mlp_modules_0_parameters_bias_"
+    shape = [2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.604
+    std = 0.135
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_11_modules_mlp_modules_3_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_11_modules_mlp_modules_3_parameters_weight_"
+    shape = [512, 2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.060
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_11_modules_mlp_modules_3_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_11_modules_mlp_modules_3_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.001
+    std = 0.119
+    data = [
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+        -0.096135,
+        -0.068096,
+        0.088653,
+        0.040375,
+        -0.204295,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_12_modules_norm1_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_5_modules_12_modules_norm1_parameters_weight_"
+    )
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.761
+    std = 0.226
+    data = [
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+        0.613443,
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+        0.652794,
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+        1.275140,
+        0.713309,
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+        1.039481,
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+        0.591069,
+        0.796112,
+        1.013432,
+        0.509618,
+        0.800869,
+        0.839935,
+        0.991522,
+        0.882276,
+        0.880279,
+        0.836057,
+        0.664539,
+        0.871167,
+        0.744040,
+        0.336016,
+        0.709328,
+        0.775164,
+        1.174695,
+        0.988251,
+        0.976504,
+        0.903592,
+        0.908493,
+        0.645527,
+        0.640523,
+        0.618995,
+        0.866061,
+        1.013558,
+        0.937071,
+        1.063637,
+        1.358167,
+        0.487281,
+        0.906330,
+        0.729549,
+        0.795825,
+        1.055529,
+        0.670178,
+        0.835325,
+        0.800020,
+        0.633876,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_12_modules_norm1_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_12_modules_norm1_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.015
+    std = 0.138
+    data = [
+        -0.151731,
+        -0.083414,
+        0.069587,
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+        0.052224,
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+        -0.057539,
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+        0.266134,
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+        -0.154381,
+        0.064871,
+        -0.084876,
+        -0.038347,
+        -0.066507,
+        -0.151331,
+        0.229892,
+        0.008140,
+        -0.029296,
+        -0.105633,
+        -0.005415,
+        -0.035344,
+        0.562759,
+        -0.029134,
+        -0.007610,
+        0.057193,
+        0.088521,
+        0.011989,
+        -0.122751,
+        -0.138143,
+        -0.043407,
+        0.140896,
+        -0.085201,
+        0.028040,
+        0.163769,
+        -0.039595,
+        0.042338,
+        -0.035097,
+        -0.054380,
+        -0.004169,
+        0.112498,
+        -0.012564,
+        -0.380428,
+        -0.022698,
+        -0.016924,
+        -0.092057,
+        -0.100190,
+        -0.218868,
+        -0.012988,
+        -0.026901,
+        0.030260,
+        0.049886,
+        -0.101248,
+        0.171618,
+        0.088967,
+        -0.018157,
+        -0.207485,
+        -0.028083,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_12_modules_attn_parameters_relative_position_bias_table_:
+    name = "L_self_modules_features_modules_5_modules_12_modules_attn_parameters_relative_position_bias_table_"
+    shape = [169, 16]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.084
+    std = 0.680
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_12_modules_attn_buffers_relative_position_index_:
+    name = "L_self_modules_features_modules_5_modules_12_modules_attn_buffers_relative_position_index_"
+    shape = [2401]
+    dtype = "torch.int64"
+    device = "cpu"
+    mean = None
+    std = None
+    min_val = 0
+    max_val = 168
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_12_modules_attn_modules_qkv_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_12_modules_attn_modules_qkv_parameters_weight_"
+    shape = [1536, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.050
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_12_modules_attn_modules_proj_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_12_modules_attn_modules_proj_parameters_weight_"
+    shape = [512, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.047
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_12_modules_attn_modules_qkv_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_12_modules_attn_modules_qkv_parameters_bias_"
+    shape = [1536]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.017
+    std = 0.464
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_12_modules_attn_modules_proj_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_12_modules_attn_modules_proj_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.214
+    data = [
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+        0.091578,
+        -0.016647,
+        -0.041034,
+        0.014640,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_12_modules_norm2_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_5_modules_12_modules_norm2_parameters_weight_"
+    )
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 1.445
+    std = 0.278
+    data = [
+        1.102434,
+        1.512519,
+        1.289527,
+        1.760861,
+        1.569743,
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+        1.357393,
+        0.953488,
+        1.562360,
+        1.215540,
+        1.531693,
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+        1.396851,
+        1.574395,
+        1.594102,
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+        1.667444,
+        1.047710,
+        1.633195,
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+        1.510251,
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+        1.215378,
+        1.473802,
+        1.891404,
+        0.884925,
+        1.671989,
+        1.213436,
+        1.546945,
+        1.737643,
+        1.042489,
+        1.653792,
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+        1.520442,
+        1.652935,
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+        1.592302,
+        1.285831,
+        1.493734,
+        2.113493,
+        1.508448,
+        1.630020,
+        1.508996,
+        1.518065,
+        1.030233,
+        1.730779,
+        1.231416,
+        1.200867,
+        1.471882,
+        2.034701,
+        1.469659,
+        1.203009,
+        1.462195,
+        1.391119,
+        1.545986,
+        1.113752,
+        1.356128,
+        1.526156,
+        1.445907,
+        1.621115,
+        1.407483,
+        1.485509,
+        1.568839,
+        1.592875,
+        1.510820,
+        1.058814,
+        1.349027,
+        1.103867,
+        1.429385,
+        1.410235,
+        1.605416,
+        2.232023,
+        1.508465,
+        1.476574,
+        1.068850,
+        1.149777,
+        1.438334,
+        0.591191,
+        1.445706,
+        1.570491,
+        1.625687,
+        1.515825,
+        1.327630,
+        1.485317,
+        1.362575,
+        1.457306,
+        1.642793,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_12_modules_norm2_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_12_modules_norm2_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.009
+    std = 0.307
+    data = [
+        -0.368647,
+        -0.440187,
+        0.120483,
+        0.641297,
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+        0.274178,
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+        0.132204,
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+        0.335662,
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+        0.133237,
+        0.141303,
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+        0.381753,
+        -0.271243,
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+        -0.362309,
+        -0.118586,
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+        -0.055044,
+        0.030363,
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+        0.297903,
+        0.223058,
+        -0.015572,
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+        0.072266,
+        -0.015264,
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+        -0.043177,
+        -0.282698,
+        -0.009627,
+        0.401937,
+        -0.714969,
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+        0.172474,
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+        -0.067109,
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+        -0.357942,
+        0.617699,
+        0.265370,
+        -0.369277,
+        -0.205398,
+        -0.189805,
+        0.166315,
+        -0.763415,
+        -0.045324,
+        -0.030962,
+        -0.170549,
+        0.921229,
+        -0.330643,
+        0.724885,
+        0.179114,
+        0.007660,
+        0.438943,
+        0.215846,
+        0.047494,
+        -0.405815,
+        -0.133533,
+        -0.354676,
+        0.019765,
+        -0.279606,
+        -0.286139,
+        0.682616,
+        -0.331194,
+        0.218744,
+        -0.268582,
+        0.117359,
+        0.083609,
+        0.145609,
+        -0.158694,
+        -0.044837,
+        0.004668,
+        0.006512,
+        -0.011388,
+        -0.048097,
+        -0.506656,
+        0.302001,
+        0.112666,
+        0.509248,
+        0.477882,
+        -0.394229,
+        0.373659,
+        0.264256,
+        0.144115,
+        -0.285713,
+        -0.516082,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_12_modules_mlp_modules_0_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_12_modules_mlp_modules_0_parameters_weight_"
+    shape = [2048, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.049
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_12_modules_mlp_modules_0_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_12_modules_mlp_modules_0_parameters_bias_"
+    shape = [2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.603
+    std = 0.130
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_12_modules_mlp_modules_3_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_12_modules_mlp_modules_3_parameters_weight_"
+    shape = [512, 2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.061
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_12_modules_mlp_modules_3_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_12_modules_mlp_modules_3_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.003
+    std = 0.168
+    data = [
+        0.238180,
+        -0.059858,
+        -0.177759,
+        -0.100621,
+        0.152979,
+        0.061857,
+        0.028157,
+        0.069783,
+        -0.257327,
+        -0.142706,
+        0.061913,
+        0.134831,
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+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_13_modules_norm1_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_5_modules_13_modules_norm1_parameters_weight_"
+    )
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.756
+    std = 0.205
+    data = [
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+        0.820280,
+        1.020591,
+        1.301699,
+        0.739559,
+        0.893287,
+        0.714658,
+        0.996621,
+        0.902069,
+        0.424228,
+        0.904907,
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+        1.164334,
+        0.767783,
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+        1.030717,
+        0.759705,
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+        1.281456,
+        0.711125,
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+        0.300703,
+        1.069093,
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+        0.423738,
+        0.637894,
+        0.794012,
+        0.697056,
+        0.518329,
+        1.261047,
+        0.775881,
+        0.887917,
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+        0.755703,
+        0.682343,
+        0.832553,
+        0.752297,
+        0.534357,
+        0.699152,
+        0.908112,
+        1.169019,
+        0.817209,
+        0.809756,
+        0.855711,
+        0.654243,
+        0.782887,
+        0.811388,
+        0.384823,
+        0.728661,
+        0.791556,
+        0.772368,
+        0.973577,
+        1.056592,
+        0.837848,
+        0.761834,
+        0.691261,
+        0.701522,
+        0.652424,
+        0.763637,
+        1.122623,
+        1.355422,
+        1.009835,
+        1.314054,
+        0.560074,
+        0.817053,
+        0.803834,
+        0.815428,
+        0.986491,
+        0.616450,
+        0.790990,
+        0.785451,
+        0.588329,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_13_modules_norm1_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_13_modules_norm1_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.017
+    std = 0.136
+    data = [
+        -0.227746,
+        -0.090543,
+        0.059091,
+        0.071566,
+        -0.061999,
+        0.054332,
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+        0.059988,
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+        0.090664,
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+        0.083591,
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+        0.563768,
+        -0.077097,
+        -0.075106,
+        -0.045731,
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+        0.200674,
+        0.346348,
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+        0.029065,
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+        -0.233068,
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+        -0.134134,
+        0.078478,
+        -0.096277,
+        -0.091986,
+        -0.071838,
+        -0.145655,
+        0.218616,
+        0.000031,
+        0.002657,
+        -0.096965,
+        0.007631,
+        -0.041953,
+        0.402641,
+        -0.056828,
+        -0.016274,
+        0.070138,
+        0.112069,
+        -0.002090,
+        -0.095284,
+        -0.101873,
+        -0.091694,
+        0.115177,
+        -0.107227,
+        0.029266,
+        0.190970,
+        -0.051487,
+        0.072874,
+        -0.006572,
+        -0.063041,
+        -0.007603,
+        0.081035,
+        -0.011765,
+        -0.238135,
+        -0.025422,
+        -0.048266,
+        -0.149673,
+        -0.058860,
+        -0.205874,
+        0.057505,
+        -0.020613,
+        0.047519,
+        0.054707,
+        -0.147980,
+        0.180958,
+        0.083258,
+        -0.031883,
+        -0.212731,
+        -0.003284,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_13_modules_attn_parameters_relative_position_bias_table_:
+    name = "L_self_modules_features_modules_5_modules_13_modules_attn_parameters_relative_position_bias_table_"
+    shape = [169, 16]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.110
+    std = 0.631
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_13_modules_attn_buffers_relative_position_index_:
+    name = "L_self_modules_features_modules_5_modules_13_modules_attn_buffers_relative_position_index_"
+    shape = [2401]
+    dtype = "torch.int64"
+    device = "cpu"
+    mean = None
+    std = None
+    min_val = 0
+    max_val = 168
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_13_modules_attn_modules_qkv_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_13_modules_attn_modules_qkv_parameters_weight_"
+    shape = [1536, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.051
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_13_modules_attn_modules_proj_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_13_modules_attn_modules_proj_parameters_weight_"
+    shape = [512, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.047
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_13_modules_attn_modules_qkv_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_13_modules_attn_modules_qkv_parameters_bias_"
+    shape = [1536]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.012
+    std = 0.500
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_13_modules_attn_modules_proj_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_13_modules_attn_modules_proj_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.003
+    std = 0.160
+    data = [
+        -0.117748,
+        -0.114101,
+        -0.047329,
+        0.001378,
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+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_13_modules_norm2_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_5_modules_13_modules_norm2_parameters_weight_"
+    )
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 2.115
+    std = 0.409
+    data = [
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+        1.524079,
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+        1.874564,
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+        2.206319,
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+        2.132689,
+        2.278201,
+        2.378165,
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+        2.048447,
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+        2.216142,
+        2.173573,
+        2.264235,
+        2.179361,
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+        2.087944,
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+        2.859057,
+        2.323376,
+        2.046816,
+        2.365988,
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+        2.631470,
+        2.209708,
+        1.378338,
+        2.402227,
+        2.214690,
+        2.314429,
+        2.314360,
+        1.899534,
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+        1.950917,
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+        1.828105,
+        2.322566,
+        1.694408,
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+        1.699957,
+        1.902348,
+        2.035076,
+        2.300554,
+        2.153045,
+        1.861611,
+        2.356694,
+        1.831270,
+        2.220302,
+        1.998067,
+        1.951280,
+        2.227818,
+        1.055423,
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+        0.172720,
+        2.344382,
+        2.077477,
+        2.543189,
+        2.327140,
+        1.979092,
+        1.296364,
+        2.246955,
+        1.738600,
+        2.171280,
+        2.141909,
+        0.847083,
+        2.026437,
+        2.326024,
+        2.172625,
+        2.128963,
+        2.312980,
+        1.632997,
+        2.432164,
+        1.709181,
+        2.151006,
+        1.824820,
+        2.283887,
+        0.736037,
+        1.826634,
+        2.226382,
+        3.007231,
+        1.192970,
+        2.460868,
+        1.781269,
+        2.092138,
+        2.687312,
+        1.482590,
+        2.238855,
+        2.051525,
+        2.309896,
+        2.307041,
+        2.181498,
+        2.223216,
+        1.883328,
+        2.114161,
+        2.936293,
+        2.170170,
+        2.629520,
+        2.257234,
+        2.169330,
+        1.536970,
+        2.436221,
+        1.931941,
+        1.756073,
+        2.122680,
+        2.972578,
+        2.206512,
+        1.773394,
+        2.169885,
+        2.207984,
+        2.265443,
+        1.666857,
+        2.071218,
+        2.155242,
+        2.106300,
+        2.214703,
+        2.132921,
+        2.202743,
+        2.374051,
+        2.314027,
+        2.207533,
+        1.565282,
+        1.946135,
+        1.599462,
+        2.045778,
+        2.097262,
+        2.392824,
+        3.458942,
+        2.224959,
+        2.272212,
+        1.470573,
+        1.670516,
+        2.126646,
+        0.767406,
+        2.119945,
+        2.292583,
+        2.312250,
+        2.158832,
+        1.979652,
+        2.134780,
+        2.038841,
+        2.126286,
+        2.404523,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_13_modules_norm2_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_13_modules_norm2_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.027
+    std = 0.449
+    data = [
+        -0.420649,
+        -0.470715,
+        0.187953,
+        0.836915,
+        -0.074725,
+        0.335456,
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+        0.146477,
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+        0.193125,
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+        0.149463,
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+        0.542231,
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+        0.070880,
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+        -0.130204,
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+        0.567046,
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+        0.709356,
+        0.405242,
+        -0.957586,
+        -0.262838,
+        -0.333198,
+        0.186788,
+        -1.257091,
+        -0.066680,
+        -0.148575,
+        -0.413377,
+        1.226445,
+        -0.344350,
+        1.034171,
+        0.185619,
+        -0.119357,
+        0.745124,
+        0.312430,
+        0.101009,
+        -0.552269,
+        -0.055638,
+        -0.554019,
+        0.112449,
+        -0.544691,
+        -0.394530,
+        0.994261,
+        -0.412330,
+        0.217452,
+        -0.417746,
+        0.206683,
+        0.171807,
+        0.133255,
+        -0.321007,
+        0.604348,
+        -0.019892,
+        -0.022333,
+        -0.035211,
+        -0.010667,
+        -0.833125,
+        0.340420,
+        0.059908,
+        0.634015,
+        0.710497,
+        -0.702675,
+        0.609069,
+        0.456548,
+        0.109791,
+        -0.500717,
+        -0.552081,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_13_modules_mlp_modules_0_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_13_modules_mlp_modules_0_parameters_weight_"
+    shape = [2048, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.001
+    std = 0.050
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_13_modules_mlp_modules_0_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_13_modules_mlp_modules_0_parameters_bias_"
+    shape = [2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.601
+    std = 0.133
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_13_modules_mlp_modules_3_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_13_modules_mlp_modules_3_parameters_weight_"
+    shape = [512, 2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.061
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_13_modules_mlp_modules_3_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_13_modules_mlp_modules_3_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.007
+    std = 0.243
+    data = [
+        0.266308,
+        -0.055407,
+        -0.176451,
+        -0.218298,
+        0.168125,
+        -0.041186,
+        0.148354,
+        -0.021732,
+        -0.303931,
+        -0.101353,
+        0.233395,
+        0.030744,
+        -0.070249,
+        0.193655,
+        -0.164622,
+        -0.366729,
+        -0.173746,
+        -0.158786,
+        -0.084324,
+        -0.211436,
+        0.212341,
+        -0.107878,
+        -0.247269,
+        -0.018881,
+        -0.038373,
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+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_14_modules_norm1_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_5_modules_14_modules_norm1_parameters_weight_"
+    )
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.823
+    std = 0.221
+    data = [
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+        1.148587,
+        0.953262,
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+        0.902799,
+        1.106477,
+        0.994812,
+        1.041469,
+        0.505582,
+        0.896588,
+        0.603325,
+        1.097580,
+        0.614863,
+        1.229174,
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+        1.032391,
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+        1.686815,
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+        0.845040,
+        0.763757,
+        1.472878,
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+        0.993895,
+        0.743307,
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+        1.002263,
+        0.784758,
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+        0.865673,
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+        0.475169,
+        0.722564,
+        0.853452,
+        0.780442,
+        0.611392,
+        1.307689,
+        0.836085,
+        1.053304,
+        1.082809,
+        0.911698,
+        0.650837,
+        0.856283,
+        1.070725,
+        0.559347,
+        0.804775,
+        0.953820,
+        1.070854,
+        1.013600,
+        0.895931,
+        0.819689,
+        0.669811,
+        0.957842,
+        0.841979,
+        0.381023,
+        0.765226,
+        0.850897,
+        1.218175,
+        1.028468,
+        1.028899,
+        0.935780,
+        0.927389,
+        0.724041,
+        0.763708,
+        0.792466,
+        0.912214,
+        1.044922,
+        0.981944,
+        1.093835,
+        1.465878,
+        0.585187,
+        0.971165,
+        0.827899,
+        0.856601,
+        1.089823,
+        0.686019,
+        0.896032,
+        0.901397,
+        0.695616,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_14_modules_norm1_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_14_modules_norm1_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.020
+    std = 0.138
+    data = [
+        -0.154776,
+        -0.050456,
+        0.080058,
+        0.091857,
+        -0.061458,
+        0.056872,
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+        0.031318,
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+        -0.064419,
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+        0.358028,
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+        -0.174340,
+        0.095846,
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+        -0.067630,
+        -0.098940,
+        -0.186856,
+        0.218159,
+        0.008187,
+        -0.011096,
+        -0.142030,
+        -0.013021,
+        -0.038778,
+        0.619918,
+        -0.033276,
+        -0.050332,
+        0.073578,
+        0.120279,
+        -0.009645,
+        -0.114227,
+        -0.140423,
+        -0.112633,
+        0.139375,
+        -0.105965,
+        0.025633,
+        0.187697,
+        -0.052218,
+        0.053180,
+        -0.052779,
+        -0.061672,
+        -0.009034,
+        0.096818,
+        -0.025831,
+        -0.259259,
+        -0.025498,
+        -0.019535,
+        -0.137588,
+        -0.087722,
+        -0.232735,
+        0.066993,
+        -0.019253,
+        0.016320,
+        0.060744,
+        -0.176798,
+        0.192560,
+        0.089238,
+        -0.036082,
+        -0.213335,
+        -0.000048,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_14_modules_attn_parameters_relative_position_bias_table_:
+    name = "L_self_modules_features_modules_5_modules_14_modules_attn_parameters_relative_position_bias_table_"
+    shape = [169, 16]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.040
+    std = 1.111
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_14_modules_attn_buffers_relative_position_index_:
+    name = "L_self_modules_features_modules_5_modules_14_modules_attn_buffers_relative_position_index_"
+    shape = [2401]
+    dtype = "torch.int64"
+    device = "cpu"
+    mean = None
+    std = None
+    min_val = 0
+    max_val = 168
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_14_modules_attn_modules_qkv_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_14_modules_attn_modules_qkv_parameters_weight_"
+    shape = [1536, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.050
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_14_modules_attn_modules_proj_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_14_modules_attn_modules_proj_parameters_weight_"
+    shape = [512, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.049
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_14_modules_attn_modules_qkv_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_14_modules_attn_modules_qkv_parameters_bias_"
+    shape = [1536]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.013
+    std = 0.486
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_14_modules_attn_modules_proj_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_14_modules_attn_modules_proj_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.004
+    std = 0.242
+    data = [
+        0.061978,
+        -0.027957,
+        0.069383,
+        -0.066021,
+        0.004013,
+        -0.129790,
+        0.177822,
+        -0.208913,
+        0.330226,
+        0.113802,
+        0.277554,
+        -0.240992,
+        0.300721,
+        -0.020479,
+        0.206258,
+        -0.116847,
+        -0.099970,
+        0.131377,
+        0.029645,
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+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_14_modules_norm2_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_5_modules_14_modules_norm2_parameters_weight_"
+    )
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 2.642
+    std = 0.509
+    data = [
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+        2.809766,
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+        2.840569,
+        2.731164,
+        2.633813,
+        3.070404,
+        2.800514,
+        2.796291,
+        2.759152,
+        2.635226,
+        2.758735,
+        2.809566,
+        3.495808,
+        1.714441,
+        2.993030,
+        2.807007,
+        2.629854,
+        2.935201,
+        2.766424,
+        2.869633,
+        2.302032,
+        2.588357,
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+        2.583001,
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+        3.643733,
+        3.160403,
+        2.577058,
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+        2.556581,
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+        3.052498,
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+        2.892221,
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+        2.872916,
+        2.642201,
+        2.689745,
+        2.308383,
+        2.863732,
+        2.194244,
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+        2.948938,
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+        2.814555,
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+        2.363947,
+        2.742350,
+        2.506435,
+        2.328085,
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+        1.319466,
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+        2.238301,
+        0.267740,
+        3.017898,
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+        3.205193,
+        2.905215,
+        2.553076,
+        1.605102,
+        2.715613,
+        2.227462,
+        2.708394,
+        2.711367,
+        1.167093,
+        2.600996,
+        2.854996,
+        2.782464,
+        2.685901,
+        3.029059,
+        1.928515,
+        3.095242,
+        2.024102,
+        2.785770,
+        2.171691,
+        2.911264,
+        0.913763,
+        2.201801,
+        2.975001,
+        3.611760,
+        1.482178,
+        3.050507,
+        2.325145,
+        2.703387,
+        3.385718,
+        1.785445,
+        2.826145,
+        2.491417,
+        2.732629,
+        2.980925,
+        2.557577,
+        2.802078,
+        2.297576,
+        2.636272,
+        3.591271,
+        2.789135,
+        3.286229,
+        2.801809,
+        2.723832,
+        1.897871,
+        3.045485,
+        2.390474,
+        2.149338,
+        2.679965,
+        3.666997,
+        2.775321,
+        2.149015,
+        2.788747,
+        2.739364,
+        2.831468,
+        2.022002,
+        2.612744,
+        2.703407,
+        2.666977,
+        2.792079,
+        2.663870,
+        2.642107,
+        2.954131,
+        2.858516,
+        2.892484,
+        1.934958,
+        2.572664,
+        2.110982,
+        2.593498,
+        2.542236,
+        3.067669,
+        4.407737,
+        2.796319,
+        2.653172,
+        1.827698,
+        2.063780,
+        2.557662,
+        0.957735,
+        2.745292,
+        2.895696,
+        2.879967,
+        2.620530,
+        2.477874,
+        2.649319,
+        2.660570,
+        2.489876,
+        3.007200,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_14_modules_norm2_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_14_modules_norm2_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.040
+    std = 0.559
+    data = [
+        -0.657412,
+        -0.521068,
+        0.365572,
+        0.821073,
+        -0.162251,
+        0.544074,
+        -0.656696,
+        0.623810,
+        0.241944,
+        -0.513576,
+        -0.820670,
+        0.284484,
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+        0.335050,
+        0.820848,
+        -0.238237,
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+        0.496898,
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+        -1.281046,
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+        0.120558,
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+        0.669461,
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+        0.668722,
+        0.497350,
+        -0.878848,
+        -0.309249,
+        -0.445748,
+        0.112460,
+        -1.645309,
+        -0.098231,
+        -0.169725,
+        -0.610750,
+        1.475635,
+        -0.480753,
+        1.227947,
+        0.253723,
+        -0.175994,
+        0.957727,
+        0.333474,
+        0.128293,
+        -0.584053,
+        -0.027689,
+        -0.827999,
+        0.167956,
+        -0.654266,
+        -0.363333,
+        1.224411,
+        -0.551481,
+        0.290260,
+        -0.700716,
+        0.259569,
+        0.218730,
+        0.164136,
+        -0.467710,
+        0.964041,
+        0.004972,
+        -0.131759,
+        -0.129279,
+        0.025121,
+        -0.842704,
+        0.399933,
+        0.042091,
+        0.800332,
+        0.823367,
+        -0.868128,
+        0.886428,
+        0.570419,
+        0.159755,
+        -0.585841,
+        -0.537045,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_14_modules_mlp_modules_0_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_14_modules_mlp_modules_0_parameters_weight_"
+    shape = [2048, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.001
+    std = 0.050
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_14_modules_mlp_modules_0_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_14_modules_mlp_modules_0_parameters_bias_"
+    shape = [2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.604
+    std = 0.130
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_14_modules_mlp_modules_3_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_14_modules_mlp_modules_3_parameters_weight_"
+    shape = [512, 2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.062
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_14_modules_mlp_modules_3_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_14_modules_mlp_modules_3_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.011
+    std = 0.331
+    data = [
+        0.427200,
+        -0.056229,
+        -0.189775,
+        -0.262620,
+        0.137455,
+        -0.001983,
+        0.302021,
+        -0.035850,
+        -0.328650,
+        0.001630,
+        0.356275,
+        0.072909,
+        -0.102417,
+        0.078830,
+        -0.080425,
+        -0.498892,
+        -0.348125,
+        -0.119357,
+        -0.140186,
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+        0.213929,
+        -0.061297,
+        -0.286516,
+        -0.017056,
+        -0.216483,
+        -0.082299,
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+        0.049025,
+        0.069739,
+        -0.388120,
+        0.243604,
+        0.147694,
+        0.130410,
+        0.202605,
+        0.208137,
+        0.070011,
+        0.091835,
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+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_15_modules_norm1_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_5_modules_15_modules_norm1_parameters_weight_"
+    )
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.810
+    std = 0.217
+    data = [
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+        0.629646,
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+        1.009436,
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+        0.792768,
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+        0.772056,
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+        0.733263,
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+        0.790467,
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+        1.203791,
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+        0.682256,
+        1.039651,
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+        0.830129,
+        0.422951,
+        1.318102,
+        1.077966,
+        1.036358,
+        0.616133,
+        0.788510,
+        1.063694,
+        0.671703,
+        1.057448,
+        0.821375,
+        0.854895,
+        0.940772,
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+        1.149222,
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+        0.875415,
+        0.689375,
+        0.567563,
+        0.954077,
+        0.961384,
+        0.996662,
+        1.330744,
+        0.789390,
+        0.954688,
+        0.795822,
+        1.020285,
+        0.975295,
+        0.435528,
+        0.978674,
+        0.555584,
+        1.007369,
+        0.615393,
+        0.997551,
+        0.785155,
+        0.941634,
+        0.734474,
+        1.186565,
+        1.101773,
+        0.831834,
+        0.790068,
+        0.967066,
+        0.603860,
+        1.130303,
+        0.793125,
+        0.631980,
+        1.336303,
+        0.802709,
+        0.884219,
+        0.886676,
+        0.650496,
+        0.877006,
+        0.308933,
+        1.064013,
+        0.936158,
+        0.465986,
+        0.706017,
+        0.953225,
+        0.745333,
+        0.563311,
+        1.315940,
+        0.872027,
+        0.930877,
+        0.915180,
+        0.851650,
+        0.620979,
+        0.810765,
+        0.791714,
+        0.541910,
+        0.792797,
+        0.984058,
+        1.192666,
+        0.930373,
+        0.844843,
+        0.901614,
+        0.655770,
+        0.929754,
+        0.917493,
+        0.320918,
+        0.773421,
+        0.804742,
+        0.905680,
+        1.042639,
+        1.099802,
+        0.892824,
+        0.843204,
+        0.770603,
+        0.761261,
+        0.712751,
+        0.800313,
+        1.164365,
+        1.358605,
+        0.952172,
+        1.464043,
+        0.605438,
+        0.917026,
+        0.823792,
+        0.873465,
+        1.062592,
+        0.698456,
+        0.875217,
+        0.864309,
+        0.637595,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_15_modules_norm1_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_15_modules_norm1_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.021
+    std = 0.145
+    data = [
+        -0.221890,
+        -0.057070,
+        0.070101,
+        0.097645,
+        -0.080634,
+        0.046978,
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+        0.041468,
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+        0.001966,
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+        0.040888,
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+        0.036212,
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+        0.103938,
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+        -0.193369,
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+        0.428780,
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+        -0.053559,
+        -0.176273,
+        0.373927,
+        0.423547,
+        -0.102513,
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+        0.036145,
+        0.040707,
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+        -0.140073,
+        0.093630,
+        -0.007219,
+        -0.108793,
+        -0.088040,
+        -0.165288,
+        0.185651,
+        0.006577,
+        0.030275,
+        -0.143184,
+        -0.002965,
+        -0.054201,
+        0.453369,
+        -0.055838,
+        -0.044308,
+        0.080273,
+        0.135313,
+        -0.010231,
+        -0.106761,
+        -0.120689,
+        -0.150075,
+        0.115824,
+        -0.125115,
+        0.025353,
+        0.195342,
+        -0.057631,
+        0.077793,
+        -0.034191,
+        -0.066687,
+        -0.016388,
+        0.073840,
+        -0.006703,
+        -0.091211,
+        -0.021655,
+        -0.050322,
+        -0.199337,
+        -0.051910,
+        -0.208536,
+        0.127733,
+        0.014778,
+        0.033042,
+        0.057382,
+        -0.219745,
+        0.190227,
+        0.085849,
+        -0.052251,
+        -0.197581,
+        -0.004132,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_15_modules_attn_parameters_relative_position_bias_table_:
+    name = "L_self_modules_features_modules_5_modules_15_modules_attn_parameters_relative_position_bias_table_"
+    shape = [169, 16]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.119
+    std = 0.946
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_15_modules_attn_buffers_relative_position_index_:
+    name = "L_self_modules_features_modules_5_modules_15_modules_attn_buffers_relative_position_index_"
+    shape = [2401]
+    dtype = "torch.int64"
+    device = "cpu"
+    mean = None
+    std = None
+    min_val = 0
+    max_val = 168
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_15_modules_attn_modules_qkv_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_15_modules_attn_modules_qkv_parameters_weight_"
+    shape = [1536, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.051
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_15_modules_attn_modules_proj_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_15_modules_attn_modules_proj_parameters_weight_"
+    shape = [512, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.047
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_15_modules_attn_modules_qkv_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_15_modules_attn_modules_qkv_parameters_bias_"
+    shape = [1536]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.006
+    std = 0.493
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_15_modules_attn_modules_proj_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_15_modules_attn_modules_proj_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.006
+    std = 0.217
+    data = [
+        0.051085,
+        -0.101652,
+        0.050424,
+        -0.006481,
+        -0.046625,
+        -0.152990,
+        0.056687,
+        -0.141693,
+        0.129648,
+        0.049506,
+        0.273283,
+        -0.192510,
+        0.183950,
+        0.069114,
+        0.123102,
+        -0.005365,
+        -0.171483,
+        0.114547,
+        0.038998,
+        -0.123610,
+        0.083521,
+        0.177270,
+        0.080326,
+        -0.012259,
+        -0.174131,
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+        -0.342800,
+        0.001745,
+        0.112435,
+        -0.103992,
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+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_15_modules_norm2_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_5_modules_15_modules_norm2_parameters_weight_"
+    )
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 2.467
+    std = 0.439
+    data = [
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+        2.665701,
+        2.446414,
+        2.823942,
+        2.468440,
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+        2.092363,
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+        2.400123,
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+        2.727425,
+        2.741249,
+        2.966076,
+        2.359357,
+        2.496783,
+        2.621571,
+        3.173514,
+        2.958818,
+        2.446178,
+        2.640381,
+        2.378253,
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+        1.702752,
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+        2.705583,
+        2.975780,
+        2.451153,
+        3.219236,
+        2.950420,
+        2.569416,
+        1.691992,
+        3.003024,
+        2.430390,
+        2.722974,
+        2.590166,
+        2.351772,
+        2.769741,
+        2.387261,
+        2.476428,
+        2.165462,
+        2.546940,
+        2.098095,
+        2.416219,
+        2.727028,
+        2.009698,
+        1.962772,
+        2.184990,
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+        2.660335,
+        2.577839,
+        2.190946,
+        2.632653,
+        2.216167,
+        2.535640,
+        2.356853,
+        2.182603,
+        2.562792,
+        1.339348,
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+        2.601756,
+        2.087904,
+        0.287980,
+        2.825277,
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+        3.011866,
+        2.631146,
+        2.354758,
+        1.741852,
+        2.559683,
+        2.057790,
+        2.526689,
+        2.427630,
+        1.294746,
+        2.399988,
+        2.651022,
+        2.588995,
+        2.408909,
+        2.862902,
+        1.909378,
+        2.831829,
+        1.941639,
+        2.619471,
+        1.988920,
+        2.665094,
+        1.024299,
+        2.046128,
+        2.768567,
+        3.325161,
+        1.604244,
+        2.929113,
+        2.195284,
+        2.444957,
+        2.923674,
+        1.764889,
+        2.634007,
+        2.315913,
+        2.474235,
+        2.707754,
+        2.441014,
+        2.670916,
+        2.153649,
+        2.479404,
+        3.305699,
+        2.606832,
+        2.843555,
+        2.556515,
+        2.483443,
+        1.677387,
+        2.695312,
+        2.186115,
+        2.082997,
+        2.550496,
+        3.534562,
+        2.509564,
+        2.024426,
+        2.728869,
+        2.593351,
+        2.653881,
+        1.877949,
+        2.377436,
+        2.493942,
+        2.576591,
+        2.482763,
+        2.469244,
+        2.557457,
+        2.702348,
+        2.678457,
+        2.606808,
+        1.912951,
+        2.484276,
+        1.931125,
+        2.470844,
+        2.499307,
+        2.866939,
+        3.928767,
+        2.580623,
+        2.476340,
+        1.589989,
+        2.008269,
+        2.304010,
+        0.955548,
+        2.558491,
+        2.739447,
+        2.713459,
+        2.425027,
+        2.391420,
+        2.618311,
+        2.401618,
+        2.450093,
+        2.869983,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_15_modules_norm2_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_15_modules_norm2_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.035
+    std = 0.499
+    data = [
+        -0.658665,
+        -0.415727,
+        0.209167,
+        0.735413,
+        -0.100172,
+        0.449110,
+        -0.542579,
+        0.454061,
+        0.173222,
+        -0.304394,
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+        0.309886,
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+        0.331787,
+        0.716619,
+        -0.225813,
+        0.006737,
+        0.591231,
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+        0.054939,
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+        -0.404686,
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+        0.145322,
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+        0.381472,
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+        -1.138900,
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+        0.131644,
+        -0.489546,
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+        -0.406048,
+        0.564379,
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+        -0.800772,
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+        0.212659,
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+        0.334634,
+        0.440357,
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+        -0.428850,
+        -0.409748,
+        0.016553,
+        -1.342929,
+        -0.066424,
+        -0.027091,
+        -0.631583,
+        1.089470,
+        -0.425154,
+        1.251708,
+        0.156398,
+        -0.095147,
+        0.913657,
+        0.325269,
+        0.130175,
+        -0.434049,
+        -0.062127,
+        -0.738472,
+        0.202601,
+        -0.756814,
+        -0.140231,
+        1.141495,
+        -0.377974,
+        0.273310,
+        -0.630621,
+        0.156400,
+        0.263416,
+        0.116036,
+        -0.401361,
+        0.634591,
+        0.255830,
+        -0.154231,
+        -0.150949,
+        0.095091,
+        -0.688835,
+        0.307088,
+        0.064266,
+        0.700098,
+        0.651852,
+        -0.917944,
+        0.846466,
+        0.533462,
+        0.042029,
+        -0.416229,
+        -0.604161,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_15_modules_mlp_modules_0_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_15_modules_mlp_modules_0_parameters_weight_"
+    shape = [2048, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.001
+    std = 0.051
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_15_modules_mlp_modules_0_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_15_modules_mlp_modules_0_parameters_bias_"
+    shape = [2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.618
+    std = 0.144
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_15_modules_mlp_modules_3_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_15_modules_mlp_modules_3_parameters_weight_"
+    shape = [512, 2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.063
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_15_modules_mlp_modules_3_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_15_modules_mlp_modules_3_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.012
+    std = 0.325
+    data = [
+        0.340260,
+        -0.111652,
+        -0.165334,
+        -0.258398,
+        0.120645,
+        -0.109803,
+        0.311173,
+        -0.127452,
+        -0.292589,
+        -0.005211,
+        0.391753,
+        0.093231,
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+        0.082151,
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+        -0.527713,
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+        -0.141141,
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+        -1.070474,
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+        0.085627,
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+        0.116540,
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+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_16_modules_norm1_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_5_modules_16_modules_norm1_parameters_weight_"
+    )
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.846
+    std = 0.234
+    data = [
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+        0.699783,
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+        0.645654,
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+        1.091116,
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+        1.017399,
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+        1.061907,
+        0.824944,
+        0.903820,
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+        0.971168,
+        1.315126,
+        0.992766,
+        1.004191,
+        0.747452,
+        1.024372,
+        1.183360,
+        1.071052,
+        1.045974,
+        0.494497,
+        0.921403,
+        0.555996,
+        1.037830,
+        0.624581,
+        1.221868,
+        0.789909,
+        1.063947,
+        0.715430,
+        1.700622,
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+        0.882593,
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+        1.469183,
+        0.586059,
+        1.077157,
+        0.781761,
+        0.635361,
+        1.059292,
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+        0.801777,
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+        0.692352,
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+        0.901298,
+        0.931622,
+        0.461428,
+        0.750906,
+        0.933324,
+        0.793815,
+        0.545377,
+        1.276545,
+        0.928760,
+        1.066993,
+        1.135721,
+        0.872169,
+        0.660979,
+        0.859663,
+        1.191699,
+        0.543948,
+        0.836825,
+        0.962192,
+        1.158364,
+        0.921646,
+        0.896985,
+        0.851384,
+        0.677786,
+        1.007589,
+        0.899106,
+        0.279343,
+        0.785931,
+        0.851326,
+        1.288058,
+        1.018655,
+        1.112575,
+        0.943693,
+        0.913466,
+        0.725611,
+        0.817262,
+        0.796690,
+        0.913343,
+        1.167075,
+        1.231442,
+        1.080832,
+        1.536463,
+        0.597192,
+        0.920502,
+        0.785665,
+        0.817843,
+        1.088445,
+        0.751265,
+        0.987834,
+        0.972360,
+        0.686041,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_16_modules_norm1_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_16_modules_norm1_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.020
+    std = 0.163
+    data = [
+        -0.202206,
+        -0.012911,
+        0.076287,
+        0.098955,
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+        0.042330,
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+        0.108227,
+        0.166389,
+        -0.085160,
+        -0.092548,
+        -0.198665,
+        0.174507,
+        0.020669,
+        0.046057,
+        -0.151469,
+        -0.040188,
+        -0.043352,
+        0.672928,
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+        -0.089763,
+        0.085511,
+        0.163812,
+        -0.034263,
+        -0.093403,
+        -0.150718,
+        -0.169408,
+        0.103697,
+        -0.114935,
+        0.019249,
+        0.185596,
+        -0.046710,
+        0.068413,
+        -0.067276,
+        -0.074329,
+        -0.024259,
+        0.076437,
+        -0.016218,
+        -0.173295,
+        -0.011161,
+        -0.020396,
+        -0.164606,
+        -0.077575,
+        -0.236872,
+        0.117717,
+        -0.000160,
+        0.020704,
+        0.079837,
+        -0.221874,
+        0.190690,
+        0.069667,
+        -0.037973,
+        -0.194182,
+        0.005601,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_16_modules_attn_parameters_relative_position_bias_table_:
+    name = "L_self_modules_features_modules_5_modules_16_modules_attn_parameters_relative_position_bias_table_"
+    shape = [169, 16]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.007
+    std = 1.412
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_16_modules_attn_buffers_relative_position_index_:
+    name = "L_self_modules_features_modules_5_modules_16_modules_attn_buffers_relative_position_index_"
+    shape = [2401]
+    dtype = "torch.int64"
+    device = "cpu"
+    mean = None
+    std = None
+    min_val = 0
+    max_val = 168
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_16_modules_attn_modules_qkv_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_16_modules_attn_modules_qkv_parameters_weight_"
+    shape = [1536, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.051
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_16_modules_attn_modules_proj_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_16_modules_attn_modules_proj_parameters_weight_"
+    shape = [512, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.049
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_16_modules_attn_modules_qkv_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_16_modules_attn_modules_qkv_parameters_bias_"
+    shape = [1536]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.018
+    std = 0.477
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_16_modules_attn_modules_proj_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_16_modules_attn_modules_proj_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.007
+    std = 0.248
+    data = [
+        0.142705,
+        -0.148279,
+        -0.014901,
+        -0.050970,
+        0.013754,
+        -0.136378,
+        0.184737,
+        -0.136999,
+        0.202099,
+        0.052202,
+        0.412618,
+        -0.147835,
+        0.256179,
+        -0.026601,
+        0.210584,
+        -0.182878,
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+        0.011234,
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+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_16_modules_norm2_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_5_modules_16_modules_norm2_parameters_weight_"
+    )
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 2.283
+    std = 0.350
+    data = [
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+        2.547997,
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+        2.229410,
+        2.211871,
+        2.315367,
+        2.733662,
+        2.784949,
+        2.368293,
+        2.472303,
+        2.222447,
+        2.399883,
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+        2.369330,
+        2.530154,
+        2.291021,
+        2.888619,
+        2.778548,
+        2.277238,
+        1.677885,
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+        2.516402,
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+        2.522120,
+        2.171292,
+        2.338038,
+        2.086686,
+        2.367061,
+        2.008834,
+        2.239038,
+        2.532413,
+        1.847213,
+        1.798207,
+        1.960701,
+        2.093586,
+        2.357342,
+        2.325796,
+        2.062399,
+        2.454134,
+        2.020003,
+        2.321481,
+        2.172249,
+        2.039758,
+        2.351914,
+        1.422330,
+        2.380189,
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+        2.000332,
+        0.310393,
+        2.704144,
+        2.335843,
+        2.791762,
+        2.402101,
+        2.190873,
+        1.790920,
+        2.352346,
+        1.949382,
+        2.298520,
+        2.383955,
+        1.353385,
+        2.177814,
+        2.494753,
+        2.412443,
+        2.213657,
+        2.540119,
+        1.776276,
+        2.658825,
+        1.745852,
+        2.419934,
+        1.930875,
+        2.564101,
+        1.119551,
+        1.926138,
+        2.543907,
+        2.839387,
+        1.510889,
+        2.657789,
+        2.183367,
+        2.302432,
+        2.517006,
+        1.636694,
+        2.384051,
+        2.148027,
+        2.339817,
+        2.381854,
+        2.324089,
+        2.401341,
+        1.989956,
+        2.212776,
+        2.782376,
+        2.408680,
+        2.573090,
+        2.340141,
+        2.377268,
+        1.564879,
+        2.465304,
+        2.083067,
+        1.902650,
+        2.306607,
+        3.252001,
+        2.217254,
+        2.081182,
+        2.464595,
+        2.368807,
+        2.488045,
+        1.797870,
+        2.271066,
+        2.181772,
+        2.386240,
+        2.287135,
+        2.257686,
+        2.291174,
+        2.372474,
+        2.537422,
+        2.397008,
+        1.898205,
+        2.373995,
+        1.921415,
+        2.246271,
+        2.253704,
+        2.682900,
+        3.442857,
+        2.357428,
+        2.328301,
+        1.564795,
+        1.921591,
+        2.160301,
+        1.027625,
+        2.398956,
+        2.550644,
+        2.444871,
+        2.292020,
+        2.315111,
+        2.431762,
+        2.219271,
+        2.283597,
+        2.593287,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_16_modules_norm2_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_16_modules_norm2_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.024
+    std = 0.469
+    data = [
+        -0.590592,
+        -0.337582,
+        0.284257,
+        0.630560,
+        -0.176135,
+        0.395952,
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+        0.418416,
+        0.299565,
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+        0.707072,
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+        0.241230,
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+        0.028617,
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+        1.068365,
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+        -0.640396,
+        -0.702535,
+        0.297845,
+        0.304075,
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+        -0.060888,
+        0.346960,
+        0.071173,
+        -0.301922,
+        -0.420732,
+        -0.094810,
+        -1.200359,
+        -0.031535,
+        0.039528,
+        -0.649095,
+        0.722550,
+        -0.240761,
+        1.322822,
+        0.110102,
+        -0.141588,
+        0.824656,
+        0.300294,
+        0.097168,
+        -0.255541,
+        -0.095972,
+        -0.718428,
+        0.213892,
+        -0.753742,
+        0.229717,
+        0.961399,
+        -0.184740,
+        0.174889,
+        -0.749580,
+        0.225862,
+        0.220747,
+        0.184807,
+        -0.342007,
+        0.413704,
+        0.325396,
+        -0.223931,
+        -0.232163,
+        0.028124,
+        -0.597215,
+        0.312978,
+        -0.019982,
+        0.511852,
+        0.497016,
+        -0.872686,
+        0.903330,
+        0.463511,
+        -0.103590,
+        -0.364666,
+        -0.524971,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_16_modules_mlp_modules_0_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_16_modules_mlp_modules_0_parameters_weight_"
+    shape = [2048, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.051
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_16_modules_mlp_modules_0_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_16_modules_mlp_modules_0_parameters_bias_"
+    shape = [2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.624
+    std = 0.153
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_16_modules_mlp_modules_3_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_16_modules_mlp_modules_3_parameters_weight_"
+    shape = [512, 2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.062
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_16_modules_mlp_modules_3_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_16_modules_mlp_modules_3_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.014
+    std = 0.329
+    data = [
+        0.305749,
+        -0.135845,
+        -0.103580,
+        -0.273236,
+        0.152847,
+        -0.059112,
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+        -0.145208,
+        -0.189966,
+        -0.036014,
+        0.344458,
+        0.130919,
+        0.016689,
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+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_17_modules_norm1_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_5_modules_17_modules_norm1_parameters_weight_"
+    )
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.791
+    std = 0.220
+    data = [
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+        0.536866,
+        0.815538,
+        0.659494,
+        0.552016,
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+        1.435151,
+        0.600892,
+        0.742654,
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+        1.207830,
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+        0.819116,
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+        0.658911,
+        1.086496,
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+        0.866431,
+        0.426610,
+        1.172670,
+        1.031867,
+        1.029002,
+        0.604143,
+        0.707690,
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+        1.052575,
+        0.816331,
+        0.875277,
+        0.951774,
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+        1.146028,
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+        0.849706,
+        0.759207,
+        0.604651,
+        0.596019,
+        0.911443,
+        1.161712,
+        0.979036,
+        1.278581,
+        0.802872,
+        0.914068,
+        0.842547,
+        0.995194,
+        0.948794,
+        0.489855,
+        0.895373,
+        0.531664,
+        0.999320,
+        0.551399,
+        1.061114,
+        0.750401,
+        0.945660,
+        0.732055,
+        1.170014,
+        0.980197,
+        0.890707,
+        0.860010,
+        1.125707,
+        0.576670,
+        1.072621,
+        0.809732,
+        0.651936,
+        1.311616,
+        0.710318,
+        0.985023,
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+        0.645333,
+        0.853684,
+        0.365645,
+        0.980440,
+        0.908396,
+        0.491074,
+        0.700105,
+        0.952082,
+        0.742112,
+        0.528836,
+        1.065428,
+        0.868347,
+        1.001054,
+        0.930237,
+        0.805765,
+        0.576834,
+        0.795702,
+        0.857525,
+        0.498354,
+        0.720984,
+        0.914728,
+        1.132649,
+        0.896987,
+        0.834416,
+        0.814884,
+        0.670344,
+        0.939680,
+        0.884124,
+        -0.055072,
+        0.724472,
+        0.769500,
+        0.967304,
+        1.010154,
+        1.080775,
+        0.916080,
+        0.865919,
+        0.694102,
+        0.863757,
+        0.693082,
+        0.747312,
+        1.090793,
+        1.245673,
+        0.945472,
+        1.467739,
+        0.569698,
+        0.851646,
+        0.707581,
+        0.823153,
+        1.032062,
+        0.674248,
+        0.840074,
+        0.866229,
+        0.601737,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_17_modules_norm1_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_17_modules_norm1_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.019
+    std = 0.180
+    data = [
+        -0.211236,
+        -0.015479,
+        0.055007,
+        0.112706,
+        -0.089966,
+        0.040257,
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+        -0.006340,
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+        0.126030,
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+        0.020294,
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+        0.035072,
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+        0.466580,
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+        -0.090971,
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+        -0.183322,
+        0.164208,
+        0.009694,
+        0.067643,
+        -0.153778,
+        -0.034135,
+        -0.033485,
+        0.487845,
+        -0.028202,
+        -0.084610,
+        0.094182,
+        0.179313,
+        -0.043393,
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+        -0.139220,
+        -0.207108,
+        0.084101,
+        -0.113208,
+        0.000155,
+        0.167099,
+        -0.041452,
+        0.115368,
+        -0.063792,
+        -0.063995,
+        -0.025109,
+        0.060531,
+        -0.014234,
+        -0.076979,
+        -0.001194,
+        -0.021138,
+        -0.155532,
+        -0.048190,
+        -0.233161,
+        0.142317,
+        0.007458,
+        0.019094,
+        0.065534,
+        -0.258238,
+        0.177029,
+        0.058691,
+        -0.038467,
+        -0.160941,
+        0.007687,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_17_modules_attn_parameters_relative_position_bias_table_:
+    name = "L_self_modules_features_modules_5_modules_17_modules_attn_parameters_relative_position_bias_table_"
+    shape = [169, 16]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.126
+    std = 0.884
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_17_modules_attn_buffers_relative_position_index_:
+    name = "L_self_modules_features_modules_5_modules_17_modules_attn_buffers_relative_position_index_"
+    shape = [2401]
+    dtype = "torch.int64"
+    device = "cpu"
+    mean = None
+    std = None
+    min_val = 0
+    max_val = 168
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_17_modules_attn_modules_qkv_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_17_modules_attn_modules_qkv_parameters_weight_"
+    shape = [1536, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.052
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_17_modules_attn_modules_proj_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_17_modules_attn_modules_proj_parameters_weight_"
+    shape = [512, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.048
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_17_modules_attn_modules_qkv_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_17_modules_attn_modules_qkv_parameters_bias_"
+    shape = [1536]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.474
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_17_modules_attn_modules_proj_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_17_modules_attn_modules_proj_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.005
+    std = 0.183
+    data = [
+        0.111477,
+        -0.213556,
+        0.043951,
+        0.014056,
+        -0.002959,
+        -0.064068,
+        0.040654,
+        -0.001156,
+        0.119535,
+        -0.034394,
+        0.308552,
+        0.072951,
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+        0.055485,
+        0.137239,
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+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_17_modules_norm2_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_5_modules_17_modules_norm2_parameters_weight_"
+    )
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 1.712
+    std = 0.209
+    data = [
+        1.340042,
+        1.809869,
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+        1.653743,
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+        1.930629,
+        2.023968,
+        1.843519,
+        1.753851,
+        1.583679,
+        1.854440,
+        1.370971,
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+        2.061553,
+        1.924163,
+        1.797752,
+        1.422778,
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+        1.654734,
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+        1.762966,
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+        1.398091,
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+        1.612087,
+        1.765574,
+        1.592473,
+        1.754137,
+        1.700599,
+        1.532587,
+        1.724091,
+        1.305343,
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+        1.808363,
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+        1.773823,
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+        1.792411,
+        1.629566,
+        1.481787,
+        1.815874,
+        1.496948,
+        1.675042,
+        1.761443,
+        1.135222,
+        1.686395,
+        1.885973,
+        1.721525,
+        1.722986,
+        1.899190,
+        1.369186,
+        1.935805,
+        1.467551,
+        1.793609,
+        1.463443,
+        1.959806,
+        0.891516,
+        1.498114,
+        1.935329,
+        1.900743,
+        1.303920,
+        1.979874,
+        1.667485,
+        1.715685,
+        1.687758,
+        1.203275,
+        1.813184,
+        1.686075,
+        1.731223,
+        1.812263,
+        1.783893,
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+        1.557599,
+        1.680690,
+        1.845733,
+        1.839998,
+        1.858820,
+        1.676205,
+        1.702652,
+        1.308929,
+        1.827762,
+        1.579790,
+        1.556064,
+        1.773342,
+        2.283535,
+        1.681302,
+        1.760713,
+        1.901094,
+        1.725858,
+        1.852154,
+        1.551307,
+        1.748772,
+        1.678437,
+        1.758511,
+        1.663886,
+        1.679330,
+        1.858430,
+        1.774599,
+        1.833596,
+        1.820445,
+        1.473674,
+        1.780346,
+        1.536450,
+        1.649935,
+        1.607386,
+        1.927671,
+        2.466982,
+        1.733700,
+        1.708514,
+        1.230467,
+        1.513280,
+        1.597224,
+        0.944589,
+        1.743713,
+        1.989685,
+        1.903215,
+        1.640057,
+        1.809340,
+        1.802683,
+        1.595343,
+        1.767587,
+        1.916452,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_17_modules_norm2_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_17_modules_norm2_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.020
+    std = 0.380
+    data = [
+        -0.427883,
+        -0.170872,
+        0.227220,
+        0.460515,
+        -0.199023,
+        0.217550,
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+        0.300886,
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+        -0.250040,
+        -0.334992,
+        -0.162265,
+        -0.891483,
+        -0.029008,
+        0.091413,
+        -0.492645,
+        0.222828,
+        -0.124174,
+        1.206448,
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+        0.435815,
+        0.238471,
+        0.030738,
+        -0.077368,
+        -0.040266,
+        -0.572378,
+        0.126530,
+        -0.573374,
+        0.463511,
+        0.568233,
+        -0.158188,
+        0.089314,
+        -0.561467,
+        0.191517,
+        0.138339,
+        0.158439,
+        -0.193792,
+        0.115062,
+        0.298316,
+        -0.187960,
+        -0.173029,
+        0.066894,
+        -0.304020,
+        0.230736,
+        -0.006053,
+        0.381326,
+        0.394140,
+        -0.631835,
+        0.692820,
+        0.342794,
+        -0.179922,
+        -0.135192,
+        -0.330133,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_17_modules_mlp_modules_0_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_17_modules_mlp_modules_0_parameters_weight_"
+    shape = [2048, 512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.051
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_17_modules_mlp_modules_0_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_17_modules_mlp_modules_0_parameters_bias_"
+    shape = [2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.638
+    std = 0.173
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_17_modules_mlp_modules_3_parameters_weight_:
+    name = "L_self_modules_features_modules_5_modules_17_modules_mlp_modules_3_parameters_weight_"
+    shape = [512, 2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.064
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_5_modules_17_modules_mlp_modules_3_parameters_bias_:
+    name = "L_self_modules_features_modules_5_modules_17_modules_mlp_modules_3_parameters_bias_"
+    shape = [512]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.010
+    std = 0.270
+    data = [
+        0.023058,
+        -0.204818,
+        -0.073060,
+        -0.163156,
+        0.126956,
+        0.062170,
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+        -0.040810,
+        -0.088666,
+        0.019783,
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+        0.070154,
+        0.176763,
+        0.124427,
+        -0.184502,
+        -0.183246,
+        0.137797,
+        0.104542,
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+        -0.237001,
+        0.018178,
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+        -0.073686,
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+        0.079190,
+        0.424418,
+        -0.090140,
+        -0.002439,
+        -0.097811,
+        -0.153521,
+        0.038722,
+        -0.020613,
+        0.127115,
+        0.029317,
+        0.017719,
+        0.000992,
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+        0.008729,
+        0.032390,
+        0.032443,
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+        0.026379,
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+        -0.038338,
+        -0.136618,
+        0.016938,
+        -0.033576,
+        -0.090124,
+    ]
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_6_modules_norm_parameters_weight_:
+    name = "L_self_modules_features_modules_6_modules_norm_parameters_weight_"
+    shape = [2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 1.285
+    std = 0.174
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_6_modules_norm_parameters_bias_:
+    name = "L_self_modules_features_modules_6_modules_norm_parameters_bias_"
+    shape = [2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.056
+    std = 0.260
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_6_modules_reduction_parameters_weight_:
+    name = "L_self_modules_features_modules_6_modules_reduction_parameters_weight_"
+    shape = [1024, 2048]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.044
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_7_modules_0_modules_norm1_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_7_modules_0_modules_norm1_parameters_weight_"
+    )
+    shape = [1024]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.359
+    std = 0.173
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_7_modules_0_modules_norm1_parameters_bias_:
+    name = "L_self_modules_features_modules_7_modules_0_modules_norm1_parameters_bias_"
+    shape = [1024]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.001
+    std = 0.095
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_7_modules_0_modules_attn_parameters_relative_position_bias_table_:
+    name = "L_self_modules_features_modules_7_modules_0_modules_attn_parameters_relative_position_bias_table_"
+    shape = [169, 32]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.090
+    std = 1.637
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_7_modules_0_modules_attn_buffers_relative_position_index_:
+    name = "L_self_modules_features_modules_7_modules_0_modules_attn_buffers_relative_position_index_"
+    shape = [2401]
+    dtype = "torch.int64"
+    device = "cpu"
+    mean = None
+    std = None
+    min_val = 0
+    max_val = 168
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_7_modules_0_modules_attn_modules_qkv_parameters_weight_:
+    name = "L_self_modules_features_modules_7_modules_0_modules_attn_modules_qkv_parameters_weight_"
+    shape = [3072, 1024]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.052
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_7_modules_0_modules_attn_modules_proj_parameters_weight_:
+    name = "L_self_modules_features_modules_7_modules_0_modules_attn_modules_proj_parameters_weight_"
+    shape = [1024, 1024]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.044
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_7_modules_0_modules_attn_modules_qkv_parameters_bias_:
+    name = "L_self_modules_features_modules_7_modules_0_modules_attn_modules_qkv_parameters_bias_"
+    shape = [3072]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.008
+    std = 0.513
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_7_modules_0_modules_attn_modules_proj_parameters_bias_:
+    name = "L_self_modules_features_modules_7_modules_0_modules_attn_modules_proj_parameters_bias_"
+    shape = [1024]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.002
+    std = 0.860
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_7_modules_0_modules_norm2_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_7_modules_0_modules_norm2_parameters_weight_"
+    )
+    shape = [1024]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 1.171
+    std = 0.644
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_7_modules_0_modules_norm2_parameters_bias_:
+    name = "L_self_modules_features_modules_7_modules_0_modules_norm2_parameters_bias_"
+    shape = [1024]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.006
+    std = 0.240
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_7_modules_0_modules_mlp_modules_0_parameters_weight_:
+    name = "L_self_modules_features_modules_7_modules_0_modules_mlp_modules_0_parameters_weight_"
+    shape = [4096, 1024]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.048
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_7_modules_0_modules_mlp_modules_0_parameters_bias_:
+    name = "L_self_modules_features_modules_7_modules_0_modules_mlp_modules_0_parameters_bias_"
+    shape = [4096]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.584
+    std = 0.221
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_7_modules_0_modules_mlp_modules_3_parameters_weight_:
+    name = "L_self_modules_features_modules_7_modules_0_modules_mlp_modules_3_parameters_weight_"
+    shape = [1024, 4096]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.055
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_7_modules_0_modules_mlp_modules_3_parameters_bias_:
+    name = "L_self_modules_features_modules_7_modules_0_modules_mlp_modules_3_parameters_bias_"
+    shape = [1024]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.757
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_7_modules_1_modules_norm1_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_7_modules_1_modules_norm1_parameters_weight_"
+    )
+    shape = [1024]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.396
+    std = 0.239
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_7_modules_1_modules_norm1_parameters_bias_:
+    name = "L_self_modules_features_modules_7_modules_1_modules_norm1_parameters_bias_"
+    shape = [1024]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.112
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_7_modules_1_modules_attn_parameters_relative_position_bias_table_:
+    name = "L_self_modules_features_modules_7_modules_1_modules_attn_parameters_relative_position_bias_table_"
+    shape = [169, 32]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.035
+    std = 1.482
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_7_modules_1_modules_attn_buffers_relative_position_index_:
+    name = "L_self_modules_features_modules_7_modules_1_modules_attn_buffers_relative_position_index_"
+    shape = [2401]
+    dtype = "torch.int64"
+    device = "cpu"
+    mean = None
+    std = None
+    min_val = 0
+    max_val = 168
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_7_modules_1_modules_attn_modules_qkv_parameters_weight_:
+    name = "L_self_modules_features_modules_7_modules_1_modules_attn_modules_qkv_parameters_weight_"
+    shape = [3072, 1024]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.052
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_7_modules_1_modules_attn_modules_proj_parameters_weight_:
+    name = "L_self_modules_features_modules_7_modules_1_modules_attn_modules_proj_parameters_weight_"
+    shape = [1024, 1024]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.042
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_7_modules_1_modules_attn_modules_qkv_parameters_bias_:
+    name = "L_self_modules_features_modules_7_modules_1_modules_attn_modules_qkv_parameters_bias_"
+    shape = [3072]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.002
+    std = 0.505
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_7_modules_1_modules_attn_modules_proj_parameters_bias_:
+    name = "L_self_modules_features_modules_7_modules_1_modules_attn_modules_proj_parameters_bias_"
+    shape = [1024]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.003
+    std = 0.763
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_7_modules_1_modules_norm2_parameters_weight_:
+    name = (
+        "L_self_modules_features_modules_7_modules_1_modules_norm2_parameters_weight_"
+    )
+    shape = [1024]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 1.104
+    std = 0.565
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_7_modules_1_modules_norm2_parameters_bias_:
+    name = "L_self_modules_features_modules_7_modules_1_modules_norm2_parameters_bias_"
+    shape = [1024]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.008
+    std = 0.229
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_7_modules_1_modules_mlp_modules_0_parameters_weight_:
+    name = "L_self_modules_features_modules_7_modules_1_modules_mlp_modules_0_parameters_weight_"
+    shape = [4096, 1024]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.049
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_7_modules_1_modules_mlp_modules_0_parameters_bias_:
+    name = "L_self_modules_features_modules_7_modules_1_modules_mlp_modules_0_parameters_bias_"
+    shape = [4096]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.618
+    std = 0.183
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_7_modules_1_modules_mlp_modules_3_parameters_weight_:
+    name = "L_self_modules_features_modules_7_modules_1_modules_mlp_modules_3_parameters_weight_"
+    shape = [1024, 4096]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.056
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_features_modules_7_modules_1_modules_mlp_modules_3_parameters_bias_:
+    name = "L_self_modules_features_modules_7_modules_1_modules_mlp_modules_3_parameters_bias_"
+    shape = [1024]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.004
+    std = 0.684
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_norm_parameters_weight_:
+    name = "L_self_modules_norm_parameters_weight_"
+    shape = [1024]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 1.381
+    std = 0.329
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_norm_parameters_bias_:
+    name = "L_self_modules_norm_parameters_bias_"
+    shape = [1024]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.007
+    std = 0.052
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_head_parameters_weight_:
+    name = "L_self_modules_head_parameters_weight_"
+    shape = [1000, 1024]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.000
+    std = 0.047
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_head_parameters_bias_:
+    name = "L_self_modules_head_parameters_bias_"
+    shape = [1000]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.058
+    std = 0.177
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