code: def __init__(
      self, in_channels, out_channels, norm_layer=nn.BatchNorm2d
  ):
      super(_SkipProject, self).__init__()
      _activation = select_activation_function("relu")
  
      self.skip_project = nn.Sequential(
          nn.Conv2d(
              in_channels, out_channels, kernel_size=1, bias=False
          ),
          norm_layer(out_channels),
          _activation(),
      )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from contextlib import ExitStack
  from .resnet import ResNetBackbone
  from .basic_blocks import select_activation_function
  from .basic_blocks import SeparableConv2d
label_desc: Use image node_id 1 to create a new _SkipProject object from inherited base classes: nn with example: obj = _SkipProject(in_channels, out_channels, norm_layer)