diff --git a/backends/arm/test/passes/test_rescale_pass.py b/backends/arm/test/passes/test_rescale_pass.py
index 90ad502378c..5725e1884b3 100644
--- a/backends/arm/test/passes/test_rescale_pass.py
+++ b/backends/arm/test/passes/test_rescale_pass.py
@@ -13,7 +13,6 @@
 from executorch.backends.arm.test import common, conftest
 from executorch.backends.arm.test.tester.arm_tester import ArmTester
 from parameterized import parameterized
-from torch.testing._internal import optests
 
 
 def test_rescale_op():
@@ -64,7 +63,7 @@ def test_nonzero_zp_for_int32():
         ),
     ]
     for sample_input in sample_inputs:
-        with pytest.raises(optests.generate_tests.OpCheckError):
+        with pytest.raises(Exception):
             torch.library.opcheck(torch.ops.tosa._rescale, sample_input)
 
 
@@ -87,7 +86,7 @@ def test_zp_outside_range():
         ),
     ]
     for sample_input in sample_inputs:
-        with pytest.raises(optests.generate_tests.OpCheckError):
+        with pytest.raises(Exception):
             torch.library.opcheck(torch.ops.tosa._rescale, sample_input)
 
 
diff --git a/backends/arm/test/runner_utils.py b/backends/arm/test/runner_utils.py
index 5a0bfe2c37c..0b1c5b05431 100644
--- a/backends/arm/test/runner_utils.py
+++ b/backends/arm/test/runner_utils.py
@@ -34,12 +34,32 @@
 from torch.fx.node import Node
 
 from torch.overrides import TorchFunctionMode
-from torch.testing._internal.common_utils import torch_to_numpy_dtype_dict
 from tosa import TosaGraph
 
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.CRITICAL)
 
+# Copied from PyTorch. 
+# From torch/testing/_internal/common_utils.py:torch_to_numpy_dtype_dict
+# To avoid a dependency on _internal stuff.
+_torch_to_numpy_dtype_dict = {
+    torch.bool      : np.bool_,
+    torch.uint8     : np.uint8,
+    torch.uint16    : np.uint16,
+    torch.uint32    : np.uint32,
+    torch.uint64    : np.uint64,
+    torch.int8      : np.int8,
+    torch.int16     : np.int16,
+    torch.int32     : np.int32,
+    torch.int64     : np.int64,
+    torch.float16   : np.float16,
+    torch.float32   : np.float32,
+    torch.float64   : np.float64,
+    torch.bfloat16  : np.float32,
+    torch.complex32 : np.complex64,
+    torch.complex64 : np.complex64,
+    torch.complex128: np.complex128
+}
 
 class QuantizationParams:
     __slots__ = ["node_name", "zp", "scale", "qmin", "qmax", "dtype"]
@@ -335,7 +355,7 @@ def run_corstone(
         output_dtype = node.meta["val"].dtype
         tosa_ref_output = np.fromfile(
             os.path.join(intermediate_path, f"out-{i}.bin"),
-            torch_to_numpy_dtype_dict[output_dtype],
+            _torch_to_numpy_dtype_dict[output_dtype],
         )
 
         output_np.append(torch.from_numpy(tosa_ref_output).reshape(output_shape))
@@ -349,7 +369,7 @@ def prep_data_for_save(
 ):
     if isinstance(data, torch.Tensor):
         data_np = np.array(data.detach(), order="C").astype(
-            torch_to_numpy_dtype_dict[data.dtype]
+            _torch_to_numpy_dtype_dict[data.dtype]
         )
     else:
         data_np = np.array(data)