class_docstr: This class specifies the interface for a model based on discrete parameters.
  
  These methods should be implemented to have access to all of the features
  of Ax.
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docstr: Obtains the point that has the best value according to the model
  prediction and its model predictions.
  
  Returns:
      (1 x d) parameter value list representing the point with the best
      value according to the model prediction. None if this function
      is not implemented for the given model.
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code: def best_point(
      self,
      n: int,
      parameter_values: List[TParamValueList],
      objective_weights: Optional[np.ndarray],
      outcome_constraints: Optional[
          Tuple[np.ndarray, np.ndarray]
      ] = None,
      fixed_features: Optional[Dict[int, TParamValue]] = None,
      pending_observations: Optional[
          List[List[TParamValueList]]
      ] = None,
      model_gen_options: Optional[TConfig] = None,
  ) -> Optional[TParamValueList]:
      """Obtains the point that has the best value according to the model
      prediction and its model predictions.
  
      Returns:
          (1 x d) parameter value list representing the point with the best
          value according to the model prediction. None if this function
          is not implemented for the given model.
      """
      return None
  
file_path: Ax/ax/models/discrete_base.py
imports: from typing import Dict, List, Optional, Tuple
  import numpy
  from ax.core.types import TGenMetadata, TParamValue, TParamValueList
  from ax.models.base import Model
  from ax.models.types import TConfig
label_desc: Use image node_id 5 for calling the DiscreteModel obj's underlying member method code with example usage: obj.best_point(n, parameter_values, objective_weights, outcome_constraints, fixed_features, pending_observations, model_gen_options) and returns: None