class_docstr: Performs mean and variance normalization of the input tensor.
  
  Arguments
  ---------
  mean_norm : True
       If True, the mean will be normalized.
  std_norm : True
       If True, the standard deviation will be normalized.
  norm_type : str
       It defines how the statistics are computed ('sentence' computes them
       at sentence level, 'batch' at batch level, 'speaker' at speaker
       level, while global computes a single normalization vector for all
       the sentences in the dataset). Speaker and global statistics are
       computed with a moving average approach.
  avg_factor : float
       It can be used to manually set the weighting factor between
       current statistics and accumulated ones.
  
  Example
  -------
  >>> import torch
  >>> norm = InputNormalization()
  >>> inputs = torch.randn([10, 101, 20])
  >>> inp_len = torch.ones([10])
  >>> features = norm(inputs, inp_len)
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docstr: Save statistic dictionary.
  
  Arguments
  ---------
  path : str
      A path where to save the dictionary.
docstr_tok: ["Save","statistic","dictionary",".","Arguments","--","--","--","--","-","path",":","str","A","path","where","to","save","the","dictionary","."]
code: def _save(self, path):
      """Save statistic dictionary.
  
      Arguments
      ---------
      path : str
          A path where to save the dictionary.
      """
      stats = self._statistics_dict()
      torch.save(stats, path)
  
imports: import math
  import torch
  import logging
  from speechbrain.utils.checkpoints import mark_as_saver, mark_as_loader, mark_as_transfer, register_checkpoint_hooks
  from speechbrain.dataio.dataio import length_to_mask
label_desc: Use image node_id 7 for calling the InputNormalization obj's underlying member method code with example usage: obj._save(path) without return types