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: Generate new candidates.
  
  Args:
      n: Number of candidates to generate.
      parameter_values: A list of possible values for each parameter.
      objective_weights: The objective is to maximize a weighted sum of
          the columns of f(x). These are the weights.
      outcome_constraints: A tuple of (A, b). For k outcome constraints
          and m outputs at f(x), A is (k x m) and b is (k x 1) such that
          A f(x) <= b.
      fixed_features: A map {feature_index: value} for features that
          should be fixed to a particular value during generation.
      pending_observations:  A list of m lists of parameterizations
          (each parameterization is a list of parameter values of length d),
          each of length k_i, for each outcome i.
      model_gen_options: A config dictionary that can contain
          model-specific options.
  
  Returns:
      2-element tuple containing
  
      - List of n generated points, where each point is represented
        by a list of parameter values.
      - List of weights for each of the n points.
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code: def gen(
      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,
  ) -> Tuple[List[TParamValueList], List[float], TGenMetadata]:
      """
      Generate new candidates.
  
      Args:
          n: Number of candidates to generate.
          parameter_values: A list of possible values for each parameter.
          objective_weights: The objective is to maximize a weighted sum of
              the columns of f(x). These are the weights.
          outcome_constraints: A tuple of (A, b). For k outcome constraints
              and m outputs at f(x), A is (k x m) and b is (k x 1) such that
              A f(x) <= b.
          fixed_features: A map {feature_index: value} for features that
              should be fixed to a particular value during generation.
          pending_observations:  A list of m lists of parameterizations
              (each parameterization is a list of parameter values of length d),
              each of length k_i, for each outcome i.
          model_gen_options: A config dictionary that can contain
              model-specific options.
  
      Returns:
          2-element tuple containing
  
          - List of n generated points, where each point is represented
            by a list of parameter values.
          - List of weights for each of the n points.
      """
      raise NotImplementedError
  
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 3 for calling the DiscreteModel obj's underlying member method code with example usage: obj.gen(n, parameter_values, objective_weights, outcome_constraints, fixed_features, pending_observations, model_gen_options) without return types