code: def __init__(
      self,
      in_channels,
      out_channels,
      reduction=1,
      norm_layer=nn.BatchNorm2d,
      **kwargs,
  ):
      super(FeatureFusion, self).__init__()
      self.conv1x1 = _ConvBNReLU(
          in_channels,
          out_channels,
          1,
          1,
          0,
          norm_layer=norm_layer,
          **kwargs,
      )
      self.channel_attention = nn.Sequential(
          nn.AdaptiveAvgPool2d(1),
          _ConvBNReLU(
              out_channels,
              out_channels // reduction,
              1,
              1,
              0,
              norm_layer=norm_layer,
              **kwargs,
          ),
          _ConvBNReLU(
              out_channels // reduction,
              out_channels,
              1,
              1,
              0,
              norm_layer=norm_layer,
              **kwargs,
          ),
          nn.Sigmoid(),
      )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from pywick.models.segmentation.da_basenets.resnet import resnet18
label_desc: Use image node_id 1 to create a new FeatureFusion object from inherited base classes: nn with example: obj = FeatureFusion(in_channels, out_channels, reduction, norm_layer)