code: def get_anchor_point_scores(self, X):
      posterior_means, posterior_stds = self.model.predict(X)
  
      return np.array(
          [
              np.random.normal(m, s)
              for m, s in zip(posterior_means, posterior_stds)
          ]
      ).flatten()
  
imports: import logging
  import numpy
  from ..experiment_design import initial_design
  from ..core.errors import FullyExploredOptimizationDomainError
  from ..core.task.space import Design_space
label_desc: Use image node_id 2 for calling the ThompsonSamplingAnchorPointsGenerator obj's underlying member method code with example usage: obj.get_anchor_point_scores(X) and returns: np