class_docstr: Linear layer with Batch Normalization.
  
  x -> linear -> BN -> o
  
  Args:
      in_features (int): number of channels in the input tensor.
      out_features (int ): number of channels in the output tensor.
      bias (bool, optional): enable/disable bias in the linear layer. Defaults to True.
      init_gain (str, optional): method to set the gain for weight initialization. Defaults to 'linear'.
class_docstr_tok: ["Linear","layer","with","Batch","Normalization",".","x","-",">","linear","-",">","BN","-",">","o","Args",":","in_features","(","int",")",":","number","of","channels","in","the","input","tensor",".","out_features","(","int",")",":","number","of","channels","in","the","output","tensor",".","bias","(","bool",",","optional",")",":","enable\/disable","bias","in","the","linear","layer",".","Defaults","to","True",".","init_gain","(","str",",","optional",")",":","method","to","set","the","gain","for","weight","initialization",".","Defaults","to","'linear","'","."]
code: def __init__(
      self, in_features, out_features, bias=True, init_gain="linear"
  ):
      super().__init__()
      self.linear_layer = torch.nn.Linear(
          in_features, out_features, bias=bias
      )
      self.batch_normalization = nn.BatchNorm1d(
          out_features, momentum=0.1, eps=1e-5
      )
      self._init_w(init_gain)
  
file_path: TTS/TTS/tts/layers/tacotron/common_layers.py
imports: import torch
  from torch import nn
  from torch.nn import functional
label_desc: Use image node_id 1 to create a new LinearBN object from inherited base classes: nn with example: obj = LinearBN(in_features, out_features, bias, init_gain)