class_docstr: Hidden Markov Model with multinomial emissions.
  
  Attributes
  ----------
  n_features : int
      Number of possible symbols emitted by the model (in the samples).
  
  monitor_ : ConvergenceMonitor
      Monitor object used to check the convergence of EM.
  
  startprob_ : array, shape (n_components, )
      Initial state occupation distribution.
  
  transmat_ : array, shape (n_components, n_components)
      Matrix of transition probabilities between states.
  
  emissionprob_ : array, shape (n_components, n_features)
      Probability of emitting a given symbol when in each state.
  
  Examples
  --------
  >>> from hmmlearn.hmm import MultinomialHMM
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code: def _init(self, X, lengths=None):
      super()._init(X, lengths=None)
      self.random_state = check_random_state(self.random_state)
      if "e" in self.init_params:
          self.emissionprob_ = self.random_state.rand(
              self.n_components, self.n_features
          )
          normalize(self.emissionprob_, axis=1)
  
imports: import logging
  import numpy
  from scipy import linalg
  from sklearn import cluster
  from sklearn.utils import check_random_state
  from .None import _emissions, _utils
  from .base import BaseHMM
  from .utils import fill_covars, normalize
label_desc: Use image node_id 2 for calling the MultinomialHMM obj's underlying member method code with example usage: obj._init(X, lengths) without return types