class_docstr: read the kaldi ark format file
class_docstr_tok: ["read","the","kaldi","ark","format","file"]
docstr: read mean variance file
  args:
      mean_variance_file: the input mean variance file
      dim_number: gen dim_number array
  return:
      -1: failed
      0: success
docstr_tok: ["read","mean","variance","file","args",":","mean_variance_file",":","the","input","mean","variance","file","dim_number",":","gen","dim_number","array","return",":","-1",":","failed","0",":","success"]
code: def _read_mean_variance(self, mean_variance_file, dim_number):
      """
      read mean variance file
      args:
          mean_variance_file: the input mean variance file
          dim_number: gen dim_number array
      return:
          -1: failed
          0: success
      """
      if not os.path.exists(mean_variance_file):
          return -1
  
      with open(mean_variance_file) as fp:
          input_list = fp.readlines()
          input_dim_length = len(input_list)
          if dim_number > input_dim_length:
              return -1
  
          self._mean_array = np.zeros([dim_number])
          self._variance_array = np.zeros([dim_number])
          for input_line_number in range(0, dim_number):
              tmp_mean, tmp_variance = (
                  input_list[input_line_number].strip().split()
              )
              self._mean_array[input_line_number] = float(tmp_mean)
              self._variance_array[input_line_number] = math.sqrt(
                  float(tmp_variance)
              )
      self._mean_variance_statu = True
      return 0
  
file_path: delta/delta/data/utils/htk_reader_lib.py
imports: import os
  import math
  import numpy
label_desc: Use image node_id 5 for calling the HtkReaderIO obj's underlying member method code with example usage: obj._read_mean_variance(mean_variance_file, dim_number) and returns: int, unknown, unknown