class_docstr: Module for fused features
code: def __init__(
      self, inter_channels=48, norm_layer=nn.BatchNorm2d, **kwargs
  ):
      super(FeatureFused, self).__init__()
      self.conv2 = nn.Sequential(
          nn.Conv2d(512, inter_channels, 1, bias=False),
          norm_layer(inter_channels),
          nn.ReLU(True),
      )
      self.conv3 = nn.Sequential(
          nn.Conv2d(1024, inter_channels, 1, bias=False),
          norm_layer(inter_channels),
          nn.ReLU(True),
      )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from pywick.models.segmentation.da_basenets.segbase import SegBaseModel
  from pywick.models.segmentation.da_basenets.fcn import _FCNHead
label_desc: Use image node_id 1 to create a new FeatureFused object from inherited base classes: nn with example: obj = FeatureFused(inter_channels, norm_layer)