class_docstr: Class to represent a Markov Chain with multiple kernels for factored state space,
  along with methods to simulate a run.
  
  Examples
  --------
  
  Create an empty Markov Chain:
  
  >>> from pgmpy.models import MarkovChain as MC
  >>> model = MC()
  
  And then add variables to it
  
  >>> model.add_variables_from(['intel', 'diff'], [2, 3])
  
  Or directly create a Markov Chain from a list of variables and their cardinalities
  
  >>> model = MC(['intel', 'diff'], [2, 3])
  
  Add transition models
  
  >>> intel_tm = {0: {0: 0.25, 1: 0.75}, 1: {0: 0.5, 1: 0.5}}
  >>> model.add_transition_model('intel', intel_tm)
  >>> diff_tm = {0: {0: 0.1, 1: 0.5, 2: 0.4}, 1: {0: 0.2, 1: 0.2, 2: 0.6 }, 2: {0: 0.7, 1: 0.15, 2: 0.15}}
  >>> model.add_transition_model('diff', diff_tm)
  
  Set a start state
  
  >>> from pgmpy.factors.discrete import State
  >>> model.set_start_state([State('intel', 0), State('diff', 2)])
  
  Sample from it
  
  >>> model.sample(size=5)
     intel  diff
  0      0     2
  1      1     0
  2      0     1
  3      1     0
  4      0     2
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docstr: Generator version of self.sample
  
  Returns
  -------
  List of State namedtuples, representing the assignment to all variables of the model.
  
  Examples
  --------
  >>> from pgmpy.models.MarkovChain import MarkovChain
  >>> from pgmpy.factors.discrete import State
  >>> model = MarkovChain()
  >>> model.add_variables_from(['intel', 'diff'], [3, 2])
  >>> intel_tm = {0: {0: 0.2, 1: 0.4, 2:0.4}, 1: {0: 0, 1: 0.5, 2: 0.5}, 2: {0: 0.3, 1: 0.3, 2: 0.4}}
  >>> model.add_transition_model('intel', intel_tm)
  >>> diff_tm = {0: {0: 0.5, 1: 0.5}, 1: {0: 0.25, 1:0.75}}
  >>> model.add_transition_model('diff', diff_tm)
  >>> gen = model.generate_sample([State('intel', 0), State('diff', 0)], 2)
  >>> [sample for sample in gen]
  [[State(var='intel', state=2), State(var='diff', state=1)],
   [State(var='intel', state=2), State(var='diff', state=0)]]
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code: def generate_sample(self, start_state=None, size=1, seed=None):
      """
      Generator version of self.sample
  
      Returns
      -------
      List of State namedtuples, representing the assignment to all variables of the model.
  
      Examples
      --------
      >>> from pgmpy.models.MarkovChain import MarkovChain
      >>> from pgmpy.factors.discrete import State
      >>> model = MarkovChain()
      >>> model.add_variables_from(['intel', 'diff'], [3, 2])
      >>> intel_tm = {0: {0: 0.2, 1: 0.4, 2:0.4}, 1: {0: 0, 1: 0.5, 2: 0.5}, 2: {0: 0.3, 1: 0.3, 2: 0.4}}
      >>> model.add_transition_model('intel', intel_tm)
      >>> diff_tm = {0: {0: 0.5, 1: 0.5}, 1: {0: 0.25, 1:0.75}}
      >>> model.add_transition_model('diff', diff_tm)
      >>> gen = model.generate_sample([State('intel', 0), State('diff', 0)], 2)
      >>> [sample for sample in gen]
      [[State(var='intel', state=2), State(var='diff', state=1)],
       [State(var='intel', state=2), State(var='diff', state=0)]]
      """
      if start_state is None:
          if self.state is None:
              self.state = self.random_state()
          # else use previously-set state
      else:
          self.set_start_state(start_state)
      # sampled.loc[0] = [self.state[var] for var in self.variables]
  
      for i in range(size):
          for j, (var, st) in enumerate(self.state):
              next_st = sample_discrete(
                  list(self.transition_models[var][st].keys()),
                  list(self.transition_models[var][st].values()),
                  seed=seed,
              )[0]
              self.state[j] = State(var, next_st)
          yield self.state[:]
  
imports: from collections import defaultdict
  import numpy
  from pandas import DataFrame
  from scipy.linalg import eig
  from pgmpy.factors.discrete import State
  from pgmpy.global_vars import logger
  from pgmpy.utils import sample_discrete
label_desc: Use image node_id 9 for calling the MarkovChain obj's underlying member method code with example usage: obj.generate_sample(start_state, size, seed) without return types