diff --git a/.github/workflows/check-urls.yml b/.github/workflows/check-urls.yml
index c4445b17..43934c0a 100644
--- a/.github/workflows/check-urls.yml
+++ b/.github/workflows/check-urls.yml
@@ -30,8 +30,8 @@ jobs:
         print_all: false
         timeout: 2
         retry_count# : 2
-        exclude_urls: https://github.com/pytorch/pytorch/pull/117009,https://github.com/huggingface/transformers/pull/29285,https://github.com/pytorch/pytorch/blob/a44f8894fa6d973693aab44a3dda079a168b05c1/torch/_decomp/decompositions.py#L1475
-        exclude_patterns: https://dumps.wikimedia.org/,https://github.com/pytorch/pytorch/pull/,https://github.com/pytorch/pytorch/blob/a44f8894fa6d973693aab44a3dda079a168b05c1/torch/_decomp/decompositions.py#L1475,https://huggingface.co/,https://huggingface.co/
+        exclude_urls: https://github.com/pytorch/pytorch/pull/117009,https://github.com/huggingface/transformers/pull/29285,https://github.com/pytorch/pytorch/blob/a44f8894fa6d973693aab44a3dda079a168b05c1/torch/_decomp/decompositions.py#L1475,https://github.com/huggingface/transformers/pull/36652
+        exclude_patterns: https://dumps.wikimedia.org/,https://github.com/pytorch/pytorch/pull/,https://github.com/pytorch/pytorch/blob/a44f8894fa6d973693aab44a3dda079a168b05c1/torch/_decomp/decompositions.py#L1475,https://huggingface.co/,https://huggingface.co/,https://github.com/huggingface/transformers/
         # force_pass : true
 
     - name: urls-checker-docs
diff --git a/_doc/recipes/plot_dynamic_shapes_nonzero.py b/_doc/recipes/plot_dynamic_shapes_nonzero.py
index d91c4593..0e6fdbf7 100644
--- a/_doc/recipes/plot_dynamic_shapes_nonzero.py
+++ b/_doc/recipes/plot_dynamic_shapes_nonzero.py
@@ -35,21 +35,23 @@ def adaptive_enc_mask(self, x_len, chunk_start_idx, left_window=0, right_window=
         mask_right = seq_range_expand < boundary_right.unsqueeze(-1)
         return mask_left & mask_right
 
-    def forward(self, x):
-        return self.adaptive_enc_mask(x.shape[1], [])
+    def forward(self, x, y):
+        return self.adaptive_enc_mask(
+            x.shape[1], torch.tensor([], dtype=torch.int64), left_window=y.shape[0]
+        )
 
 
 model = Model()
-x = torch.rand((5, 8))
-y = model(x)
-print(f"x.shape={x.shape}, y.shape={y.shape}")
+x, y = torch.rand((2, 546)), torch.rand((18,))
+z = model(x, y)
+print(f"y.shape={x.shape}, y.shape={y.shape}, z.shape={z.shape}")
 
 # %%
 # Export
 # ++++++
 
 DYN = torch.export.Dim.DYNAMIC
-ep = torch.export.export(model, (x,), dynamic_shapes=(({0: DYN, 1: DYN}),))
+ep = torch.export.export(model, (x, y), dynamic_shapes=({0: DYN, 1: DYN}, {0: DYN}))
 print(ep)
 
 
diff --git a/onnx_diagnostic/helpers/torch_test_helper.py b/onnx_diagnostic/helpers/torch_test_helper.py
index 2bc4f2e2..7f1cbea8 100644
--- a/onnx_diagnostic/helpers/torch_test_helper.py
+++ b/onnx_diagnostic/helpers/torch_test_helper.py
@@ -39,7 +39,8 @@ def _forward_(*args, _f=None, _context=None, **kwargs):
 def steal_forward(model: torch.nn.Module, with_shape: bool = True, with_min_max: bool = False):
     """
     The necessary modification to steem forward method and prints out inputs
-    and outputs. See example :ref:`l-plot-tiny-llm-export`.
+    and outputs using :func:`onnx_diagnostic.helpers.string_type`.
+    See example :ref:`l-plot-tiny-llm-export`.
     """
     context = dict(
         iteration=0,
@@ -58,7 +59,10 @@ def steal_forward(model: torch.nn.Module, with_shape: bool = True, with_min_max:
 
 
 def is_torchdynamo_exporting() -> bool:
-    """Tells if torch is exporting a model."""
+    """
+    Tells if :epkg:`torch` is exporting a model.
+    Relies on ``torch.compiler.is_exporting()``.
+    """
     import torch
 
     if not hasattr(torch.compiler, "is_exporting"):
@@ -77,7 +81,7 @@ def is_torchdynamo_exporting() -> bool:
 
 
 def to_numpy(tensor: "torch.Tensor"):  # noqa: F821
-    """Converts a torch tensor to numy."""
+    """Converts a :class:`torch.Tensor` to :class:`numpy.ndarray`."""
     try:
         return tensor.numpy()
     except TypeError:
@@ -309,10 +313,7 @@ def forward(self, input_ids):
 
 
 def to_any(value: Any, to_value: Union[torch.dtype, torch.device]) -> Any:
-    """
-    Applies torch.to is applicable.
-    Goes recursively.
-    """
+    """Applies torch.to if applicable. Goes recursively."""
     if isinstance(value, (torch.nn.Module, torch.Tensor)):
         return value.to(to_value)
     if isinstance(value, list):
@@ -344,9 +345,7 @@ def to_any(value: Any, to_value: Union[torch.dtype, torch.device]) -> Any:
 
 
 def torch_deepcopy(value: Any) -> Any:
-    """
-    Makes a deepcopy.
-    """
+    """Makes a deepcopy."""
     if value is None:
         return None
     if isinstance(value, (int, float, str)):