code: def __init__(
      self,
      in_channels,
      out_channels,
      kernel_size=1,
      stride=1,
      padding=None,
      dilation=1,
      bias=True,
      w_init_gain="linear",
  ):
      super(ConvNorm, self).__init__()
      if padding is None:
          assert kernel_size % 2 == 1
          padding = int(dilation * (kernel_size - 1) / 2)
  
      self.conv = torch.nn.Conv1d(
          in_channels,
          out_channels,
          kernel_size=kernel_size,
          stride=stride,
          padding=padding,
          dilation=dilation,
          bias=bias,
      )
  
      torch.nn.init.xavier_uniform_(
          self.conv.weight,
          gain=torch.nn.init.calculate_gain(w_init_gain),
      )
  
imports: import torch
  from librosa.filters import mel
  from .audio_processing import dynamic_range_compression
  from .audio_processing import dynamic_range_decompression
  from .stft import STFT
  from .utils import get_mask_from_lengths
label_desc: Use image node_id 1 to create a new ConvNorm object from inherited base classes: torch.nn with example: obj = ConvNorm(in_channels, out_channels, kernel_size, stride, padding, dilation, bias, w_init_gain)