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 store_inverse(self):
      pass
  
file_path: TTS/TTS/tts/layers/generic/normalization.py
imports: import torch
  from torch import nn
label_desc: Use image node_id 3 for calling the ActNorm obj's underlying member method code with example usage: obj.store_inverse() without return types