code: def __init__(
      self, in_channels, width=512, norm_layer=nn.BatchNorm2d, **kwargs
  ):
      super(JPU, self).__init__()
  
      self.conv5 = nn.Sequential(
          nn.Conv2d(in_channels[-1], width, 3, padding=1, bias=False),
          norm_layer(width),
          nn.ReLU(True),
      )
      self.conv4 = nn.Sequential(
          nn.Conv2d(in_channels[-2], width, 3, padding=1, bias=False),
          norm_layer(width),
          nn.ReLU(True),
      )
      self.conv3 = nn.Sequential(
          nn.Conv2d(in_channels[-3], width, 3, padding=1, bias=False),
          norm_layer(width),
          nn.ReLU(True),
      )
  
      self.dilation1 = nn.Sequential(
          SeparableConv2d(
              3 * width, width, 3, padding=1, dilation=1, bias=False
          ),
          norm_layer(width),
          nn.ReLU(True),
      )
      self.dilation2 = nn.Sequential(
          SeparableConv2d(
              3 * width, width, 3, padding=2, dilation=2, bias=False
          ),
          norm_layer(width),
          nn.ReLU(True),
      )
      self.dilation3 = nn.Sequential(
          SeparableConv2d(
              3 * width, width, 3, padding=4, dilation=4, bias=False
          ),
          norm_layer(width),
          nn.ReLU(True),
      )
      self.dilation4 = nn.Sequential(
          SeparableConv2d(
              3 * width, width, 3, padding=8, dilation=8, bias=False
          ),
          norm_layer(width),
          nn.ReLU(True),
      )
  
file_path: pywick/pywick/models/segmentation/da_basenets/jpu.py
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new JPU object from inherited base classes: nn with example: obj = JPU(in_channels, width, norm_layer)