hunk.code_window:         return x
   def _create_resnet_with_cfg(variant, default_cfg, pretrained=False, **kwargs):
       assert isinstance(default_cfg, dict)
       load_strict, features = True, False
       out_indices = None
       if kwargs.pop('features_only', False):
  
hunk.labels: keep keep keep keep keep replace keep keep
hunk.after_edit:     features = False
  
hunk.file_path: timm/models/resnet.py
hunk.type: replace
file: import pytest
  import torch
  import platform
  import os
  import fnmatch
  
  from timm import list_models, create_model, set_scriptable
  
  
  if 'GITHUB_ACTIONS' in os.environ and 'Linux' in platform.system():
      # GitHub Linux runner is slower and hits memory limits sooner than MacOS, exclude bigger models
      EXCLUDE_FILTERS = ['*efficientnet_l2*', '*resnext101_32x48d']
  else:
      EXCLUDE_FILTERS = []
  MAX_FWD_SIZE = 384
  MAX_BWD_SIZE = 128
  MAX_FWD_FEAT_SIZE = 448
  
  
  @pytest.mark.timeout(120)
  @pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS))
  @pytest.mark.parametrize('batch_size', [1])
  def test_model_forward(model_name, batch_size):
      """Run a single forward pass with each model"""
      model = create_model(model_name, pretrained=False)
      model.eval()
  
      input_size = model.default_cfg['input_size']
      if any([x > MAX_FWD_SIZE for x in input_size]):
          # cap forward test at max res 448 * 448 to keep resource down
          input_size = tuple([min(x, MAX_FWD_SIZE) for x in input_size])
      inputs = torch.randn((batch_size, *input_size))
      outputs = model(inputs)
  
      assert outputs.shape[0] == batch_size
      assert not torch.isnan(outputs).any(), 'Output included NaNs'
  
  
  @pytest.mark.timeout(120)
  # DLA models have an issue TBD, add them to exclusions
  @pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS + ['dla*']))
  @pytest.mark.parametrize('batch_size', [2])
  def test_model_backward(model_name, batch_size):
      """Run a single forward pass with each model"""
      model = create_model(model_name, pretrained=False, num_classes=42)
      num_params = sum([x.numel() for x in model.parameters()])
      model.eval()
  
      input_size = model.default_cfg['input_size']
      if any([x > MAX_BWD_SIZE for x in input_size]):
          # cap backward test at 128 * 128 to keep resource usage down
          input_size = tuple([min(x, MAX_BWD_SIZE) for x in input_size])
      inputs = torch.randn((batch_size, *input_size))
      outputs = model(inputs)
      outputs.mean().backward()
      for n, x in model.named_parameters():
          assert x.grad is not None, f'No gradient for {n}'
      num_grad = sum([x.grad.numel() for x in model.parameters() if x.grad is not None])
  
      assert outputs.shape[-1] == 42
      assert num_params == num_grad, 'Some parameters are missing gradients'
      assert not torch.isnan(outputs).any(), 'Output included NaNs'
  
  
  @pytest.mark.timeout(120)
  @pytest.mark.parametrize('model_name', list_models())
  @pytest.mark.parametrize('batch_size', [1])
  def test_model_default_cfgs(model_name, batch_size):
      """Run a single forward pass with each model"""
      model = create_model(model_name, pretrained=False)
      model.eval()
      state_dict = model.state_dict()
      cfg = model.default_cfg
  
      classifier = cfg['classifier']
      first_conv = cfg['first_conv']
      pool_size = cfg['pool_size']
      input_size = model.default_cfg['input_size']
  
      if all([x <= MAX_FWD_FEAT_SIZE for x in input_size]) and \
              not any([fnmatch.fnmatch(model_name, x) for x in EXCLUDE_FILTERS]):
          # pool size only checked if default res <= 448 * 448 to keep resource down
          input_size = tuple([min(x, MAX_FWD_FEAT_SIZE) for x in input_size])
          outputs = model.forward_features(torch.randn((batch_size, *input_size)))
          assert outputs.shape[-1] == pool_size[-1] and outputs.shape[-2] == pool_size[-2]
      assert any([k.startswith(classifier) for k in state_dict.keys()]), f'{classifier} not in model params'
      assert any([k.startswith(first_conv) for k in state_dict.keys()]), f'{first_conv} not in model params'
  
  
  EXCLUDE_JIT_FILTERS = [
      '*iabn*', 'tresnet*',  # models using inplace abn unlikely to ever be scriptable
      'dla*', 'hrnet*',  # hopefully fix at some point
  ]
  
  
  @pytest.mark.timeout(120)
  @pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_JIT_FILTERS))
  @pytest.mark.parametrize('batch_size', [1])
  def test_model_forward_torchscript(model_name, batch_size):
      """Run a single forward pass with each model"""
      with set_scriptable(True):
          model = create_model(model_name, pretrained=False)
      model.eval()
      input_size = (3, 128, 128)  # jit compile is already a bit slow and we've tested normal res already...
      model = torch.jit.script(model)
      outputs = model(torch.randn((batch_size, *input_size)))
  
      assert outputs.shape[0] == batch_size
      assert not torch.isnan(outputs).any(), 'Output included NaNs'
  