docstr: Layer norm for the 2nd dimension of the input.
  Args:
      channels (int): number of channels (2nd dimension) of the input.
      eps (float): to prevent 0 division
  
  Shapes:
      - input: (B, C, T)
      - output: (B, C, T)
docstr_tok: ["Layer","norm","for","the","2nd","dimension","of","the","input",".","Args",":","channels","(","int",")",":","number","of","channels","(","2nd","dimension",")","of","the","input",".","eps","(","float",")",":","to","prevent","0","division","Shapes",":","-","input",":","(","B",",","C",",","T",")","-","output",":","(","B",",","C",",","T",")"]
code: def __init__(self, channels, eps=1e-4):
      """Layer norm for the 2nd dimension of the input.
      Args:
          channels (int): number of channels (2nd dimension) of the input.
          eps (float): to prevent 0 division
  
      Shapes:
          - input: (B, C, T)
          - output: (B, C, T)
      """
      super().__init__()
      self.channels = channels
      self.eps = eps
  
      self.gamma = nn.Parameter(torch.ones(1, channels, 1) * 0.1)
      self.beta = nn.Parameter(torch.zeros(1, channels, 1))
  
file_path: TTS/TTS/tts/layers/generic/normalization.py
imports: import torch
  from torch import nn
label_desc: Use image node_id 1 to create a new LayerNorm object from inherited base classes: nn with example: obj = LayerNorm(channels, eps)