class_docstr: Normalize mean and variance of values based on empirical values.
  
  Args:
      shape (int or tuple of int): Shape of input values except batch axis.
      batch_axis (int): Batch axis.
      eps (float): Small value for stability.
      dtype (dtype): Dtype of input values.
      until (int or None): If this arg is specified, the link learns input
          values until the sum of batch sizes exceeds it.
class_docstr_tok: ["Normalize","mean","and","variance","of","values","based","on","empirical","values",".","Args",":","shape","(","int","or","tuple","of","int",")",":","Shape","of","input","values","except","batch","axis",".","batch_axis","(","int",")",":","Batch","axis",".","eps","(","float",")",":","Small","value","for","stability",".","dtype","(","dtype",")",":","Dtype","of","input","values",".","until","(","int","or","None",")",":","If","this","arg","is","specified",",","the","link","learns","input","values","until","the","sum","of","batch","sizes","exceeds","it","."]
code: def mean(self):
      return torch.squeeze(self._mean, self.batch_axis).clone()
  
file_path: pfrl/pfrl/nn/empirical_normalization.py
imports: import numpy
  import torch
  from torch import nn
label_desc: Use image node_id 2 for calling the EmpiricalNormalization obj's underlying member method code with example usage: obj.mean() and returns: torch