code: def __init__(
      self,
      norm_layer="batch_norm",
      use_bias=False,
      resnet_blocks=7,
      tanh=True,
      filters=[32, 64, 128, 128, 128, 64],
      input_channels=3,
      output_channels=3,
      append_smoothers=False,
  ):
      super().__init__()
      assert norm_layer in [
          None,
          "batch_norm",
          "instance_norm",
      ], "norm_layer should be None, 'batch_norm' or 'instance_norm', not {}".format(
          norm_layer
      )
      self.norm_layer = None
      if norm_layer == "batch_norm":
          self.norm_layer = nn.BatchNorm2d
      elif norm_layer == "instance_norm":
          self.norm_layer = nn.InstanceNorm2d
  
      # filters = [f//3 for f in filters]
      self.use_bias = use_bias
      self.resnet_blocks = resnet_blocks
      self.append_smoothers = append_smoothers
  
      stride1 = 2
      stride2 = 2
      self.conv0 = self.relu_layer(
          in_filters=input_channels,
          out_filters=filters[0],
          kernel_size=7,
          stride=1,
          padding=3,
          bias=self.use_bias,
          norm_layer=self.norm_layer,
          nonlinearity=grelu,
      )
  
      self.conv1 = self.relu_layer(
          in_filters=filters[0],
          out_filters=filters[1],
          kernel_size=3,
          stride=stride1,
          padding=1,
          bias=self.use_bias,
          norm_layer=self.norm_layer,
          nonlinearity=grelu,
      )
  
      self.conv2 = self.relu_layer(
          in_filters=filters[1],
          out_filters=filters[2],
          kernel_size=3,
          stride=stride2,
          padding=1,
          bias=self.use_bias,
          norm_layer=self.norm_layer,
          nonlinearity=grelu,
      )
  
      self.resnets = nn.ModuleList()
      for i in range(self.resnet_blocks):
          self.resnets.append(
              self.resnet_block(
                  in_filters=filters[2],
                  out_filters=filters[2],
                  kernel_size=3,
                  stride=1,
                  padding=1,
                  bias=self.use_bias,
                  norm_layer=self.norm_layer,
                  nonlinearity=grelu,
              )
          )
  
      self.upconv2 = self.upconv_layer_upsample_and_conv(
          in_filters=filters[3] + filters[2],
          # in_filters=filters[3], # disable skip-connections
          out_filters=filters[4],
          scale_factor=stride2,
          kernel_size=3,
          stride=1,
          padding=1,
          bias=self.use_bias,
          norm_layer=self.norm_layer,
          nonlinearity=grelu,
      )
  
      self.upconv1 = self.upconv_layer_upsample_and_conv(
          in_filters=filters[4] + filters[1],
          # in_filters=filters[4],  # disable skip-connections
          out_filters=filters[4],
          scale_factor=stride1,
          kernel_size=3,
          stride=1,
          padding=1,
          bias=self.use_bias,
          norm_layer=self.norm_layer,
          nonlinearity=grelu,
      )
  
      self.conv_11 = nn.Sequential(
          nn.Conv2d(
              in_channels=filters[0] + filters[4] + input_channels,
              # in_channels=filters[4],  # disable skip-connections
              out_channels=filters[5],
              kernel_size=7,
              stride=1,
              padding=3,
              bias=self.use_bias,
              padding_mode="zeros",
          ),
          grelu,
      )
  
      if self.append_smoothers:
          self.conv_11_a = nn.Sequential(
              nn.Conv2d(
                  filters[5],
                  filters[5],
                  kernel_size=3,
                  bias=self.use_bias,
                  padding=1,
                  padding_mode="zeros",
              ),
              grelu,
              # replace with variable
              nn.BatchNorm2d(num_features=filters[5]),
              nn.Conv2d(
                  filters[5],
                  filters[5],
                  kernel_size=3,
                  bias=self.use_bias,
                  padding=1,
                  padding_mode="zeros",
              ),
              grelu,
          )
  
      if tanh:
          self.conv_12 = nn.Sequential(
              nn.Conv2d(
                  filters[5],
                  output_channels,
                  kernel_size=1,
                  stride=1,
                  padding=0,
                  bias=True,
                  padding_mode="zeros",
              ),
              # torchvision.transforms.Grayscale(num_output_channels=3),
              nn.Sigmoid(),
          )
      else:
          self.conv_12 = nn.Conv2d(
              filters[5],
              output_channels,
              kernel_size=1,
              stride=1,
              padding=0,
              bias=True,
              padding_mode="zeros",
          )
  
imports: import torch
  import torch.nn
  from torch.autograd import Variable
  from torch.nn import functional
  import torchvision
  from torchvision import models
  import pytorch_lightning
label_desc: Use image node_id 1 to create a new Generator object from inherited base classes: pl with example: obj = Generator(norm_layer, use_bias, resnet_blocks, tanh, filters, input_channels, output_channels, append_smoothers)