code: def __init__(
      self, in_channels, growth_rate, n_layers, upsample=False
  ):
      super(DenseBlock, self).__init__()
      self.upsample = upsample
      self.layers = nn.ModuleList(
          [
              DenseLayer(in_channels + i * growth_rate, growth_rate)
              for i in range(n_layers)
          ]
      )
  
      self.SE_upsample1 = nn.Conv2d(
          growth_rate * n_layers,
          growth_rate * n_layers // 16,
          kernel_size=1,
      )
      self.SE_upsample2 = nn.Conv2d(
          growth_rate * n_layers // 16,
          growth_rate * n_layers,
          kernel_size=1,
      )
      self.SE1 = nn.Conv2d(
          (in_channels + growth_rate * n_layers),
          (in_channels + growth_rate * n_layers) // 16,
          kernel_size=1,
      )
      self.SE2 = nn.Conv2d(
          (in_channels + growth_rate * n_layers) // 16,
          (in_channels + growth_rate * n_layers),
          kernel_size=1,
      )
  
imports: import torch
  import torch.functional
  import torch.nn
label_desc: Use image node_id 1 to create a new DenseBlock object from inherited base classes: nn with example: obj = DenseBlock(in_channels, growth_rate, n_layers, upsample)