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 __init__(
      self,
      stats_file: str,
      norm_means: bool = True,
      norm_vars: bool = True,
      eps: float = 1.0e-20,
  ):
      super().__init__()
      self.norm_means = norm_means
      self.norm_vars = norm_vars
  
      self.stats_file = stats_file
      stats = np.load(stats_file)
  
      stats = stats.astype(float)
      assert (len(stats) - 1) % 2 == 0, stats.shape
  
      count = stats.flatten()[-1]
      mean = stats[: (len(stats) - 1) // 2] / count
      var = stats[(len(stats) - 1) // 2 : -1] / count - mean * mean
      std = np.maximum(np.sqrt(var), eps)
  
      self.register_buffer(
          "bias", torch.from_numpy(-mean.astype(np.float32))
      )
      self.register_buffer(
          "scale", torch.from_numpy(1 / std.astype(np.float32))
      )
  
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 1 to create a new GlobalMVN object from inherited base classes: torch.nn with example: obj = GlobalMVN(stats_file, norm_means, norm_vars, eps)