class_docstr: An implementation of a stochastic model that uniformly samples from data samples. By randomly returning a sample
  from the training data set, this model represents a parameter free representation of the marginal distribution of
  the training data. However, it will not generate unseen data points. For this, consider :py:class:`BayesianGaussianMixtureDistribution <dowhy.gcm.BayesianGaussianMixtureDistribution>`.
code: def clone(self):
      return EmpiricalDistribution()
  
imports: import warnings
  from typing import Dict, Optional, Tuple, Union
  import numpy
  import scipy
  from scipy.stats import norm, rv_continuous, rv_discrete
  from sklearn.cluster import KMeans
  from sklearn.metrics import silhouette_score
  from sklearn.mixture import BayesianGaussianMixture
  from dowhy.gcm.causal_mechanisms import StochasticModel
  from dowhy.gcm.util.general import shape_into_2d
label_desc: Use image node_id 5 for calling the EmpiricalDistribution obj's underlying member method code with example usage: obj.clone() and returns: EmpiricalDistribution