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.
code: def __init__(
      self,
      modelset=None,
      segset=None,
      start=None,
      stop=None,
      stat0=None,
      stat1=None,
  ):
      if modelset is None:  # For creating empty stat server
          self.modelset = numpy.empty(0, dtype="|O")
          self.segset = numpy.empty(0, dtype="|O")
          self.start = numpy.empty(0, dtype="|O")
          self.stop = numpy.empty(0, dtype="|O")
          self.stat0 = numpy.array([], dtype=STAT_TYPE)
          self.stat1 = numpy.array([], dtype=STAT_TYPE)
      else:
          self.modelset = modelset
          self.segset = segset
          self.start = start
          self.stop = stop
          self.stat0 = stat0
          self.stat1 = stat1
  
imports: import numpy
  import copy
  import pickle
  from scipy import linalg
label_desc: Use image node_id 1 to create a new StatObject_SB object  with example: obj = StatObject_SB(modelset, segset, start, stop, stat0, stat1)