code: def __init__(
      self,
      kernel_size,
      in_channels,
      inner_channels,
      out_channels,
      out_padding=(1, 1),
  ):
      super(Fire_Up, self).__init__()
      padds = int(kernel_size / 2)
      self.Conv1 = nn.Conv2d(
          in_channels,
          inner_channels,
          kernel_size=3,
          stride=1,
          padding=1,
      )
      if not out_padding:
          out_padding = (1, 1)
      # self.ConvT1=nn.ConvTranspose2d(inner_channels,out_channels,kernel_size=1,stride=2,padding=0,output_padding=out_padding)
      self.ConvT4 = nn.ConvTranspose2d(
          inner_channels,
          out_channels,
          kernel_size=kernel_size,
          stride=2,
          padding=padds,
          output_padding=out_padding,
      )
      # self.ConvT8=nn.ConvTranspose2d(inner_channels,out_channels,kernel_size=5,stride=2,padding=2,output_padding=out_padding)
      self.Conv2 = nn.Conv2d(
          out_channels, out_channels, 3, padding=1, stride=1
      )
      self.bn1 = nn.BatchNorm2d(out_channels)
      self.act = nn.PReLU()
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new Fire_Up object from inherited base classes: nn with example: obj = Fire_Up(kernel_size, in_channels, inner_channels, out_channels, out_padding)