code: def _unified_diff(a: str, b: str) -> str:
      """Return a string containing the unified diff of two strings.
  
      This function calls a patched version of `difflib.unified_diff` with `autojunk` set
      to `False` for `difflib.SequenceMatcher` class. More details can be found in
      `_patch_difflib_sequence_matcher_init` function.
  
      Args:
          a: The first string.
          b: The second string.
  
      Returns:
          The unified diff of the two strings. If there is no diff, return "<no diff>".
  
      Example::
  
          >>> a = '''class GraphModule(torch.nn.Module):
          ...     def forward(self, input_ids : torch.Tensor, attention_mask : torch.Tensor):
          ...         # File: /modeling.py:770, code: input_ids = input_ids.view(-1, input_shape[-1])
          ...         view = input_ids.view(-1, 3);  input_ids = None
          ... '''
          >>> b = '''class <lambda>(torch.nn.Module):
          ...     def forward(self, input_ids: i64[1, 3], attention_mask: i64[1, 3]):
          ...         # File: /modeling.py:770, code: input_ids = input_ids.view(-1, input_shape[-1])
          ...         view: i64[1, 3] = torch.ops.aten.view.default(input_ids, [-1, 3]);  input_ids = None
          ... '''
          >>> print(_unified_diff(a, b))
          ---
          +++
          @@ -1,4 +1,4 @@
          -class GraphModule(torch.nn.Module):
          -    def forward(self, input_ids : torch.Tensor, attention_mask : torch.Tensor):
          +class <lambda>(torch.nn.Module):
          +    def forward(self, input_ids: i64[1, 3], attention_mask: i64[1, 3]):
                  # File: /modeling.py:770, code: input_ids = input_ids.view(-1, input_shape[-1])
          -        view = input_ids.view(-1, 3);  input_ids = None
          +        view: i64[1, 3] = torch.ops.aten.view.default(input_ids, [-1, 3]);  input_ids = None
      """
  
      a_list = a.splitlines(keepends=True)
      b_list = b.splitlines(keepends=True)
  
      with _patch_difflib_sequence_matcher_init():
          # Set `n` to `sys.maxsize` to show entire graph when there is a diff.
          diff = "".join(
              difflib.unified_diff(a_list, b_list, n=sys.maxsize)
          )
  
      if not diff:
          return "<no diff>"
      return diff
  
imports: from __future__ import annotations
  import abc
  import contextlib
  import dataclasses
  import difflib
  import io
  import logging
  import sys
  from typing import Any, Callable, Optional, Tuple
  import torch
  import torch.fx
  from torch._subclasses import fake_tensor
  from torch.fx.experimental.proxy_tensor import maybe_disable_fake_tensor_mode
  from torch.onnx._internal import _beartype
  from torch.onnx._internal.fx import diagnostics, onnxfunction_dispatcher
label_desc: Use image node_id 2 for calling a global function with example usage: _unified_diff(a, b) and returns: diff, str