code: def test_segmentation_model(model_fn, dev):
      set_rng_seed(0)
      defaults = {
          "num_classes": 10,
          "weights_backbone": None,
          "input_shape": (1, 3, 32, 32),
      }
      model_name = model_fn.__name__
      kwargs = {**defaults, **_model_params.get(model_name, {})}
      input_shape = kwargs.pop("input_shape")
  
      model = model_fn(**kwargs)
      model.eval().to(device=dev)
      # RNG always on CPU, to ensure x in cuda tests is bitwise identical to x in cpu tests
      x = torch.rand(input_shape).to(device=dev)
      with torch.no_grad(), freeze_rng_state():
          out = model(x)
  
      def check_out(out):
          prec = 0.01
          try:
              # We first try to assert the entire output if possible. This is not
              # only the best way to assert results but also handles the cases
              # where we need to create a new expected result.
              _assert_expected(out.cpu(), model_name, prec=prec)
          except AssertionError:
              # Unfortunately some segmentation models are flaky with autocast
              # so instead of validating the probability scores, check that the class
              # predictions match.
              expected_file = _get_expected_file(model_name)
              expected = torch.load(expected_file, weights_only=True)
              torch.testing.assert_close(
                  out.argmax(dim=1),
                  expected.argmax(dim=1),
                  rtol=prec,
                  atol=prec,
                  check_device=False,
              )
              return False  # Partial validation performed
  
          return True  # Full validation performed
  
      full_validation = check_out(out["out"])
  
      _check_jit_scriptable(
          model,
          (x,),
          unwrapper=script_model_unwrapper.get(model_name, None),
          eager_out=out,
      )
      _check_fx_compatible(model, x, eager_out=out)
  
      if dev == "cuda":
          with torch.cuda.amp.autocast(), torch.no_grad(), freeze_rng_state():
              out = model(x)
              # See autocast_flaky_numerics comment at top of file.
              if model_name not in autocast_flaky_numerics:
                  full_validation &= check_out(out["out"])
  
      if not full_validation:
          msg = (
              f"The output of {test_segmentation_model.__name__} could only be partially validated. "
              "This is likely due to unit-test flakiness, but you may "
              "want to do additional manual checks if you made "
              "significant changes to the codebase."
          )
          warnings.warn(msg, RuntimeWarning)
          pytest.skip(msg)
  
      _check_input_backprop(model, x)
  
imports: import contextlib
  import functools
  import operator
  import os
  import pkgutil
  import platform
  import sys
  import warnings
  from collections import OrderedDict
  from tempfile import TemporaryDirectory
  from typing import Any
  import pytest
  import torch
  import torch.fx
  import torch.nn
  from _utils_internal import get_relative_path
  from common_utils import cpu_and_cuda, freeze_rng_state, map_nested_tensor_object, needs_cuda, set_rng_seed
  from PIL import Image
  from torchvision import models, transforms
  from torchvision.models import get_model_builder, list_models
label_desc: Use image node_id 23 for calling a global function with example usage: test_segmentation_model(model_fn, dev) and returns: True, False