code: def __init__(self, in_channels, out_channels, dropout=False):
      super(Dilated_UNetEnc, self).__init__()
  
      layers = [
          nn.Conv2d(
              in_channels,
              out_channels,
              3,
              padding=1,
              dilation=1,
              bias=False,
          ),
          nn.BatchNorm2d(out_channels),
          nn.ReLU(inplace=True),
          nn.Conv2d(
              out_channels,
              out_channels,
              3,
              padding=1,
              dilation=1,
              bias=False,
          ),
          nn.BatchNorm2d(out_channels),
          nn.ReLU(inplace=True),
          nn.MaxPool2d(2, stride=2, ceil_mode=True),
      ]
      if dropout:
          layers += [nn.Dropout(0.5)]
  
      self.down = nn.Sequential(*layers)
  
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_UNetEnc object from inherited base classes: nn with example: obj = Dilated_UNetEnc(in_channels, out_channels, dropout)