class_docstr: Activation Normalization bijector as an alternative to Batch Norm. It computes
  mean and std from a sample data in advance and it uses these values
  for normalization at training.
  
  Args:
      channels (int): input channels.
      ddi (False): data depended initialization flag.
  
  Shapes:
      - inputs: (B, C, T)
      - outputs: (B, C, T)
class_docstr_tok: ["Activation","Normalization","bijector","as","an","alternative","to","Batch","Norm",".","It","computes","mean","and","std","from","a","sample","data","in","advance","and","it","uses","these","values","for","normalization","at","training",".","Args",":","channels","(","int",")",":","input","channels",".","ddi","(","False",")",":","data","depended","initialization","flag",".","Shapes",":","-","inputs",":","(","B",",","C",",","T",")","-","outputs",":","(","B",",","C",",","T",")"]
code: def __init__(
      self, channels, ddi=False, **kwargs
  ):  # pylint: disable=unused-argument
      super().__init__()
      self.channels = channels
      self.initialized = not ddi
  
      self.logs = nn.Parameter(torch.zeros(1, channels, 1))
      self.bias = 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 ActNorm object from inherited base classes: nn with example: obj = ActNorm(channels, ddi)