class_docstr: A utility class for PLDA class used for statistics calculations.
  
  This is also used to pack deep embeddings and meta-information in one object.
  
  Arguments
  ---------
  modelset : list
      List of model IDs for each session as an array of strings.
  segset : list
      List of session IDs as an array of strings.
  start : int
      Index of the first frame of the segment.
  stop : int
      Index of the last frame of the segment.
  stat0 : tensor
      An ndarray of float64. Each line contains 0-th order statistics
      from the corresponding session.
  stat1 : tensor
      An ndarray of float64. Each line contains 1-st order statistics
      from the corresponding session.
docstr: Compute and return the total covariance matrix of the first-order
  statistics.
docstr_tok: ["Compute","and","return","the","total","covariance","matrix","of","the","first-order","statistics","."]
code: def get_total_covariance_stat1(self):
      """Compute and return the total covariance matrix of the first-order
      statistics.
      """
      C = self.stat1 - self.stat1.mean(axis=0)
      return numpy.dot(C.transpose(), C) / self.stat1.shape[0]
  
imports: import numpy
  import copy
  import pickle
  from scipy import linalg
label_desc: Use image node_id 8 for calling the StatObject_SB obj's underlying member method code with example usage: obj.get_total_covariance_stat1() and returns: unknown