code: def __init__(
      self,
      in_channels,
      inter_channels,
      nclass,
      norm_layer=nn.BatchNorm2d,
      **kwargs,
  ):
      super(_BiSeHead, self).__init__()
      self.block = nn.Sequential(
          _ConvBNReLU(
              in_channels,
              inter_channels,
              3,
              1,
              1,
              norm_layer=norm_layer,
              **kwargs,
          ),
          nn.Dropout(0.1),
          nn.Conv2d(inter_channels, nclass, 1),
      )
  
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 _BiSeHead object from inherited base classes: nn with example: obj = _BiSeHead(in_channels, inter_channels, nclass, norm_layer)