docstr: From and initial design, it selects the location using (marginal) Thompson sampling
  using the predictive distribution of a model
  
  model: NOTE THAT THE MODEL HERE IS is a GPyOpt model: returns mean and standard deviation
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code: def __init__(self, space, design_type, model, num_samples=25000):
      """
      From and initial design, it selects the location using (marginal) Thompson sampling
      using the predictive distribution of a model
  
      model: NOTE THAT THE MODEL HERE IS is a GPyOpt model: returns mean and standard deviation
      """
      super(ThompsonSamplingAnchorPointsGenerator, self).__init__(
          space, design_type, num_samples
      )
      self.model = model
  
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 1 to create a new ThompsonSamplingAnchorPointsGenerator object from inherited base classes: AnchorPointsGenerator with example: obj = ThompsonSamplingAnchorPointsGenerator(space, design_type, model, num_samples)