class_docstr: This class uses Expectation-Maximization to learn parameters of a single latent variable in a bayesian network.
  We do so by also allowing the user to CONSTRAINT the optimisation
  These are elements that help the algorithm find a local optimal point closer to the point we think
  
  The setting is:
  Input:
      - a StructureModel representing the whole network or any sub-graph containing the Markov Blanket of the LV
      - data as a dataframe. The LV must be in the dataframe, with missing values represented by `np.nan`s
      - constraints:
          - Box - A hard constraint; forbids the solution to be outside of certain boundaries
          - Priors - establishes Dirichlet priors to every parameter
  run:
      - using the method `run` or manually alternating over E and M steps)
  Result:
      - CPTs involving the latent variable, learnt by EM, found in the attribute `cpds`
      - CPTs not involving the LV not learned (They must be learned separately by MLE.
      This is faster and the result is the same)
  
  Example:
  >>> em = EMSingleLatentVariable(sm=sm, data=data, lv_name=lv_name, node_states=node_states)
  >>> em.run() # run EM until convergence
  >>> # or run E and M steps separately
  >>> for i in range(10): # Run EM 10 times
  >>>     em.e_step()
  >>>     em.m_step()
docstr: Maximization step. It boils down to normalising the likelihood table previously created
  
  $$ \theta_{[X | U]} = M[X, U] / M[U] = M[X, U] / \sum_X M[X, U] $$
  
  Returns:
      New updated CPDs
docstr_tok: ["Maximization","step",".","It","boils","down","to","normalising","the","likelihood","table","previously","created","$","$","\\theta_","{","[","X","|","U","]","}","=","M","[","X",",","U","]","\/","M","[","U","]","=","M","[","X",",","U","]","\/","\\sum_X","M","[","X",",","U","]","$","$","Returns",":","New","updated","CPDs"]
code: def m_step(self) -> Dict[str, pd.DataFrame]:
      """
      Maximization step. It boils down to normalising the likelihood table previously created
  
      $$ \\theta_{[X | U]} = M[X, U] / M[U] = M[X, U] / \\sum_X M[X, U] $$
  
      Returns:
          New updated CPDs
      """
      self._old_cpds = self.cpds  # Store old CPDs
      self.cpds = {
          node: self._normalise(self._sufficient_stats[node])
          for node in self.valid_nodes
      }
      return self.cpds
  
imports: import logging
  import os
  from time import time
  from typing import Dict, List, Tuple, Union
  import numpy
  import pandas
  from joblib import Parallel, delayed
  from causalnex.structure import StructureModel
  from causalnex.utils.data_utils import chunk_data, count_unique_rows
  from causalnex.utils.pgmpy_utils import cpd_multiplication
label_desc: Use image node_id 5 for calling the EMSingleLatentVariable obj's underlying member method code with example usage: obj.m_step() and returns: self