class_docstr: Apply global mean and variance normalization
  
  Args:
      stats_file(str): npy file of 1-dim array or text file.
          From the _first element to
          the {(len(array) - 1) / 2}th element are treated as
          the sum of features,
          and the rest excluding the last elements are
          treated as the sum of the square value of features,
          and the last elements eqauls to the number of samples.
      std_floor(float):
class_docstr_tok: ["Apply","global","mean","and","variance","normalization","Args",":","stats_file","(","str",")",":","npy","file","of","1-dim","array","or","text","file",".","From","the","_first","element","to","the","{","(","len","(","array",")","-","1",")","\/","2","}","th","element","are","treated","as","the","sum","of","features",",","and","the","rest","excluding","the","last","elements","are","treated","as","the","sum","of","the","square","value","of","features",",","and","the","last","elements","eqauls","to","the","number","of","samples",".","std_floor","(","float",")",":"]
code: def extra_repr(self):
      return (
          f"stats_file={self.stats_file}, "
          f"norm_means={self.norm_means}, norm_vars={self.norm_vars}"
      )
  
imports: from typing import List, Tuple, Union
  import librosa
  import numpy
  import torch
  from torch_complex.tensor import ComplexTensor
  from espnet.nets.pytorch_backend.nets_utils import make_pad_mask
label_desc: Use image node_id 2 for calling the GlobalMVN obj's underlying member method code with example usage: obj.extra_repr() and returns: str, self, str, self, str, self