code: def __init__(
      self, kernel_size, in_channels, inner_channels, out_channels
  ):
      super(Fire_Down, self).__init__()
      dilations = [1, 3, 5]
      self.Conv1 = nn.Conv2d(
          in_channels,
          inner_channels,
          kernel_size=kernel_size,
          stride=1,
          padding=dilations[0],
          dilation=dilations[0],
      )
      self.Conv4 = nn.Conv2d(
          in_channels,
          inner_channels,
          kernel_size=kernel_size,
          stride=1,
          padding=dilations[1],
          dilation=dilations[1],
      )
      self.Conv8 = nn.Conv2d(
          in_channels,
          inner_channels,
          kernel_size=kernel_size,
          stride=1,
          padding=dilations[2],
          dilation=dilations[2],
      )
      self.Conv_f3 = nn.Conv2d(
          3 * inner_channels,
          out_channels,
          kernel_size=kernel_size,
          stride=2,
          padding=1,
      )
      self.Conv_f1 = nn.Conv2d(
          out_channels, out_channels, kernel_size=1, stride=1, padding=0
      )
      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_Down object from inherited base classes: nn with example: obj = Fire_Down(kernel_size, in_channels, inner_channels, out_channels)