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: """ Conv2d + BN + Act
  
  Hacked together by Ross Wightman
  """
  from torch import nn as nn
  
  from .create_conv2d import create_conv2d
  from .create_norm_act import convert_norm_act_type
  
  
  class ConvBnAct(nn.Module):
      def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding='', dilation=1, groups=1,
                   norm_layer=nn.BatchNorm2d, norm_kwargs=None, act_layer=nn.ReLU, apply_act=True,
                   drop_block=None, aa_layer=None):
          super(ConvBnAct, self).__init__()
          use_aa = aa_layer is not None
          self.conv = create_conv2d(
              in_channels, out_channels, kernel_size, stride=1 if use_aa else stride,
              padding=padding, dilation=dilation, groups=groups, bias=False)
  
          # NOTE for backwards compatibility with models that use separate norm and act layer definitions
          norm_act_layer, norm_act_args = convert_norm_act_type(norm_layer, act_layer, norm_kwargs)
          self.bn = norm_act_layer(out_channels, apply_act=apply_act, drop_block=drop_block, **norm_act_args)
          self.aa = aa_layer(channels=out_channels) if stride == 2 and use_aa else None
  
      def forward(self, x):
          x = self.conv(x)
          x = self.bn(x)
          if self.aa is not None:
              x = self.aa(x)
          return x
  