code: def __init__(
      self, in_features: int, out_features: int, bias: bool = False
  ):
      super().__init__()
      self.linear = nn.Linear(in_features, out_features, bias)
  
      nn.init.xavier_uniform_(self.linear.weight)
      if bias:
          nn.init.constant_(self.linear.bias, 0.0)
  
imports: import math
  from typing import Tuple
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from TTS.tts.layers.delightful_tts.conv_layers import ConvNorm
label_desc: Use image node_id 1 to create a new LinearNorm object from inherited base classes: nn with example: obj = LinearNorm(in_features, out_features, bias)