diff --git a/tutorials/developer_api_guide/print_op_and_shapes.py b/tutorials/developer_api_guide/print_op_and_shapes.py
new file mode 100644
index 0000000000..0be26fd941
--- /dev/null
+++ b/tutorials/developer_api_guide/print_op_and_shapes.py
@@ -0,0 +1,35 @@
+import torch
+
+linear_shapes = []
+from torch.overrides import TorchFunctionMode
+class TorchFunctionLoggingMode(TorchFunctionMode):
+    def __torch_function__(cls, func, types, args=(), kwargs=None):
+        if kwargs is None:
+            kwargs = {}
+        if func is torch.nn.functional.linear:
+            input_tensor, weight_tensor, bias = (
+                args[0],
+                args[1],
+                args[2] if len(args) > 2 else None,
+            )
+            flattened_input_tensor = input_tensor.view(-1, input_tensor.shape[-1])
+            M, K = flattened_input_tensor.shape[0], flattened_input_tensor.shape[1]
+            assert K == weight_tensor.shape[1]
+            N = weight_tensor.shape[0]
+            print(f"TORCH_FUNC={str(func)} (M, K, N):", M, K, N)
+            linear_shapes.append((M, K, N))
+        else:
+            arg_shape = args[0].shape if len(args) > 0 and isinstance(args[0], torch.Tensor) else None
+            print(f"TORCH_FUNC={str(func)} args[0] shape:", arg_shape)
+        return func(*args, **kwargs)
+
+# NOTE: Modify this with your own model
+from torchvision import models
+m = models.vit_b_16(weights=models.ViT_B_16_Weights.IMAGENET1K_V1)
+example_inputs = (torch.randn(1, 3, 224, 224),)
+
+with TorchFunctionLoggingMode():
+     m(*example_inputs)
+
+print()
+print("all linear shapes (M, K, N):", linear_shapes)