code: def __init__(
      self,
      inChannel,
      num_classes,
      init_features,
      network_depth,
      bottleneck_layers,
  ):
      super(Dilated_UNet, self).__init__()
  
      self.network_depth = network_depth
      self.bottleneck_layers = bottleneck_layers
      skip_connection_channel_counts = []
  
      self.add_module(
          "firstconv",
          nn.Conv2d(
              in_channels=inChannel,
              out_channels=init_features,
              kernel_size=3,
              stride=1,
              padding=1,
              bias=True,
          ),
      )
  
      self.encodingBlocks = nn.ModuleList([])
      features = init_features
  
      for i in range(self.network_depth):
          self.encodingBlocks.append(
              Dilated_UNetEnc(features, 2 * features)
          )
  
          skip_connection_channel_counts.insert(0, 2 * features)
          features *= 2
      final_encoding_channels = skip_connection_channel_counts[0]
  
      self.bottleNecks = nn.ModuleList([])
      for i in range(self.bottleneck_layers):
          dilation_factor = 1
          self.bottleNecks.append(
              Dilated_Bottleneck_block(
                  final_encoding_channels,
                  final_encoding_channels,
                  dilation_rate=dilation_factor,
              )
          )
  
      self.decodingBlocks = nn.ModuleList([])
      for i in range(self.network_depth):
          if i == 0:
              prev_deconv_channels = final_encoding_channels
          self.decodingBlocks.append(
              UNetDec(
                  prev_deconv_channels
                  + skip_connection_channel_counts[i],
                  skip_connection_channel_counts[i],
              )
          )
          prev_deconv_channels = skip_connection_channel_counts[i]
  
      self.final = nn.Conv2d(64, num_classes, 1)
  
file_path: pywick/pywick/models/segmentation/testnets/unet_se.py
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new Dilated_UNet object from inherited base classes: nn with example: obj = Dilated_UNet(inChannel, num_classes, init_features, network_depth, bottleneck_layers)