class_docstr: Class for sampling methods specific to Bayesian Models
  
  Parameters
  ----------
  model: instance of BayesianNetwork
      model on which inference queries will be computed
class_docstr_tok: ["Class","for","sampling","methods","specific","to","Bayesian","Models","Parameters","--","--","--","--","--","model",":","instance","of","BayesianNetwork","model","on","which","inference","queries","will","be","computed"]
code: def __init__(self, model):
      super(BayesianModelSampling, self).__init__(model)
  
imports: import itertools
  from collections import namedtuple
  import networkx
  import numpy
  import pandas
  import torch
  from joblib import Parallel, delayed
  from tqdm.auto import tqdm
  from pgmpy import config
  from pgmpy.factors import factor_product
  from pgmpy.models import BayesianNetwork, MarkovChain, MarkovNetwork
  from pgmpy.sampling import BayesianModelInference, _return_samples
  from pgmpy.utils.mathext import sample_discrete, sample_discrete_maps
label_desc: Use image node_id 1 to create a new BayesianModelSampling object from inherited base classes: BayesianModelInference with example: obj = BayesianModelSampling(model)