code: def __init__(
      self,
      in_channels,
      nclass,
      ncodes=32,
      se_loss=True,
      norm_layer=nn.BatchNorm2d,
      norm_kwargs=None,
      **kwargs,
  ):
      super(EncModule, self).__init__()
      self.se_loss = se_loss
      self.encoding = nn.Sequential(
          nn.Conv2d(in_channels, in_channels, 1, bias=False),
          norm_layer(
              in_channels,
              **({} if norm_kwargs is None else norm_kwargs),
          ),
          nn.ReLU(True),
          Encoding(D=in_channels, K=ncodes),
          nn.BatchNorm1d(ncodes),
          nn.ReLU(True),
          Mean(dim=1),
      )
      self.fc = nn.Sequential(
          nn.Linear(in_channels, in_channels), nn.Sigmoid()
      )
      if self.se_loss:
          self.selayer = nn.Linear(in_channels, nclass)
  
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 EncModule object from inherited base classes: nn with example: obj = EncModule(in_channels, nclass, ncodes, se_loss, norm_layer, norm_kwargs)