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: Sum the zero- and first-order statistics per model and store them
  in a new StatObject_SB.
  Returns a StatObject_SB object with the statistics summed per model
  and a numpy array with session_per_model.
docstr_tok: ["Sum","the","zero-","and","first-order","statistics","per","model","and","store","them","in","a","new","StatObject_SB",".","Returns","a","StatObject_SB","object","with","the","statistics","summed","per","model","and","a","numpy","array","with","session_per_model","."]
code: def sum_stat_per_model(self):
      """Sum the zero- and first-order statistics per model and store them
      in a new StatObject_SB.
      Returns a StatObject_SB object with the statistics summed per model
      and a numpy array with session_per_model.
      """
  
      sts_per_model = StatObject_SB()
      sts_per_model.modelset = numpy.unique(
          self.modelset
      )  # nd: get uniq spkr ids
      sts_per_model.segset = copy.deepcopy(sts_per_model.modelset)
      sts_per_model.stat0 = numpy.zeros(
          (sts_per_model.modelset.shape[0], self.stat0.shape[1]),
          dtype=STAT_TYPE,
      )
      sts_per_model.stat1 = numpy.zeros(
          (sts_per_model.modelset.shape[0], self.stat1.shape[1]),
          dtype=STAT_TYPE,
      )
  
      # Keep this. may need this in future (Nauman)
      # sts_per_model.start = numpy.empty(
      #    sts_per_model.segset.shape, "|O"
      # )  # ndf: restructure this
      # sts_per_model.stop = numpy.empty(sts_per_model.segset.shape, "|O")
  
      session_per_model = numpy.zeros(
          numpy.unique(self.modelset).shape[0]
      )
  
      # For each model sum the stats
      for idx, model in enumerate(sts_per_model.modelset):
          sts_per_model.stat0[idx, :] = self.get_model_stat0(model).sum(
              axis=0
          )
          sts_per_model.stat1[idx, :] = self.get_model_stat1(model).sum(
              axis=0
          )
          session_per_model[idx] += self.get_model_stat1(model).shape[0]
      return sts_per_model, session_per_model
  
imports: import numpy
  import copy
  import pickle
  from scipy import linalg
label_desc: Use image node_id 11 for calling the StatObject_SB obj's underlying member method code with example usage: obj.sum_stat_per_model() and returns: sts_per_model,  session_per_model