code: def __init__(
      self,
      in_channels,
      out_channels,
      kernel_size,
      stride=1,
      padding=0,
      dilation=1,
      groups=1,
      init_scale=1.0,
      polyak_decay=0.9995,
  ):
      super(WeightNormConv2d, self).__init__(
          in_channels,
          out_channels,
          kernel_size,
          stride,
          padding,
          dilation,
          groups,
      )
  
      self.V = self.weight
      self.g = Parameter(torch.Tensor(out_channels))
      self.b = self.bias
  
      self.register_buffer("V_avg", torch.zeros(self.V.size()))
      self.register_buffer("g_avg", torch.zeros(out_channels))
      self.register_buffer("b_avg", torch.zeros(out_channels))
  
      self.init_scale = init_scale
      self.polyak_decay = polyak_decay
      self.reset_parameters()
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn import Parameter
label_desc: Use image node_id 1 to create a new WeightNormConv2d object from inherited base classes: nn with example: obj = WeightNormConv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, init_scale, polyak_decay)