code: def test_torch_compile():
      """
      Test ensures that allow_ops_in_compiled_graph allows compiling in a single graph
      Additionally we ensure that after compilation cache works properly
       (by changing shapes and patterns)
      We additionally check that pack/unpack still can be handled
       despite variable number of inputs/outputs
      """
      if not is_backend_tested("torch"):
          pytest.skip()
      import torch
      from torch import nn
      from einops import repeat, reduce, pack, unpack, einsum
      from einops._torch_specific import allow_ops_in_compiled_graph
  
      allow_ops_in_compiled_graph()
  
      class TorchModuleWithOperations(nn.Module):
          def __init__(self) -> None:
              super().__init__()
  
          def forward(self, x_abc, suffix=""):
              a, b, c = x_abc.shape
  
              def suf(pattern):
                  parts = pattern.split()
                  return " ".join(
                      [
                          p if p[-1] not in "acd" else p + suffix
                          for p in parts
                      ]
                  )
  
              # patterns look a bit strange because names a, c, d will be modified on every run
              # by suf function
              x_abcd = repeat(x_abc, suf("a b c -> a b c 4"))
              x_abc = reduce(x_abcd, suf("a b c d -> a b c"), "min")
              x_abdc, ps = pack(
                  [x_abc] * (2 + len(suffix)), suf("a b * c")
              )
              x_array = unpack(
                  rearrange(x_abdc, suf("a b d c -> (a b ) 1 c d")),
                  ps,
                  "ab one1 c *",
              )
              x1 = x_array[0] + len(x_array)
              x1 = rearrange(x1, suf("(a b ) 1 c -> a b c"), b=b)
              addition = einsum(
                  x_abc, x_abcd, suf("a b c , a b c d -> d")
              )[0]
              return x1 + addition
  
      original = TorchModuleWithOperations()
      compiled = torch.compile(
          original, fullgraph=True, backend="aot_eager"
      )
      for size in [10, 20, 40]:
          x = torch.rand([size, size + 1, size + 2])
          for suffix in ["", "suf1", "other_suffix"]:
              result1 = compiled(x, suffix)
              result2 = original(x, suffix)
              assert torch.allclose(result1, result2)
  
imports: import sys
  import unittest
  from doctest import testmod
  from typing import Dict, List, Optional
  import numpy
  import pytest
  from parameterized import parameterized, parameterized_class
  import einops
  import einops.layers
  import einops.parsing
  from einops._backends import AbstractBackend
  from einops.einops import rearrange, parse_shape, _optimize_transformation
  from .None import collect_test_backends, is_backend_tested
label_desc: Use image node_id 5 for calling a global function with example usage: test_torch_compile() and returns: unknown, str