hunk.code_window: @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.train()
  
hunk.labels: keep keep add keep keep keep keep
hunk.after_edit:     input_size = _get_input_size(model_name=model_name, target=TARGET_BWD_SIZE)
       if max(input_size) > MAX_BWD_SIZE:
           pytest.skip("Fixed input size model > limit.")
  
hunk.file_path: tests/test_models.py
hunk.type: add
file: DEFAULT_CROP_PCT = 0.875
  IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
  IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
  IMAGENET_INCEPTION_MEAN = (0.5, 0.5, 0.5)
  IMAGENET_INCEPTION_STD = (0.5, 0.5, 0.5)
  IMAGENET_DPN_MEAN = (124 / 255, 117 / 255, 104 / 255)
  IMAGENET_DPN_STD = tuple([1 / (.0167 * 255)] * 3)
  