class_docstr: trainer based on M-step summary statistics can add mini-batch statistics
  for a full-batch update.
class_docstr_tok: ["trainer","based","on","M-step","summary","statistics","can","add","mini-batch","statistics","for","a","full-batch","update","."]
code: def add(self, x):
      log_incomplete, qz = self.model(mx.nd.array(x))
      nz, sum_x, sum_x2 = self.model.m_step(x, qz.asnumpy())
      self.nz = self.nz + nz
      self.sum_x = self.sum_x + sum_x
      self.sum_x2 = self.sum_x2 + sum_x2
      return log_incomplete
  
file_path: gluonts/src/gluonts/nursery/gmm_tpp/gmm_base.py
imports: import time
  import mxnet
  from mxnet import gluon
  import numpy
label_desc: Use image node_id 3 for calling the GMMTrainer obj's underlying member method code with example usage: obj.add(x) and returns: log_incomplete