class_docstr: an two-layer
class_docstr_tok: ["an","two-layer"]
code: def __init__(self, in_channels, growth_rate):
      super(Dense_layer, self).__init__()
      # self.bn0=nn.BatchNorm2d(in_channels)
      self.Conv0 = nn.Conv2d(
          in_channels, in_channels + growth_rate, 3, stride=1, padding=1
      )
      self.bn1 = nn.BatchNorm2d(in_channels + growth_rate)
      self.Conv1 = nn.Conv2d(
          in_channels + growth_rate,
          growth_rate,
          kernel_size=3,
          stride=1,
          padding=1,
          bias=False,
      )
  
      self.bn2 = nn.BatchNorm2d(in_channels + growth_rate)
      self.Conv2 = nn.Conv2d(
          in_channels + growth_rate,
          in_channels,
          kernel_size=3,
          stride=1,
          padding=1,
      )
      self.bn3 = nn.BatchNorm2d(in_channels)
  
      # self.Conv1=nn.Conv2d(in_channels+growth_rate,growth_rate,kernel_size=3,stride=1,padding=1,bias=False)
  
      self.act = nn.PReLU()
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new Dense_layer object from inherited base classes: nn with example: obj = Dense_layer(in_channels, growth_rate)