code: def _get_optimal_number_of_components(X: np.ndarray) -> int:
      current_best = 0
      current_best_num_components = 1
      num_best_in_succession = 0
      try:
          for i in range(2, int(np.sqrt(X.shape[0] / 2))):
              kmeans = KMeans(n_clusters=i).fit(X)
              coefficient = silhouette_score(
                  X, kmeans.labels_, sample_size=5000
              )
  
              if coefficient > current_best:
                  current_best = coefficient
                  current_best_num_components = i
                  num_best_in_succession = 0
              else:
                  num_best_in_succession += 1
  
              if num_best_in_succession >= 3:
                  break
      except ValueError:
          # This error is typically raised when the data is discrete and all points are assigned to less cluster than
          # specified. It can also happen due to duplicated points. In these cases, the current best solution should
          # be sufficient.
          return current_best_num_components
  
      return current_best_num_components
  
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 3 for calling the BayesianGaussianMixtureDistribution obj's underlying member method code with example usage: obj._get_optimal_number_of_components(X) and returns: current_best_num_components, current_best_num_components