class_docstr: A class for defining parameter distributions.
  
  Intended for robust optimization use cases. This could be used to specify the
  distribution of an environmental variable or the distribution of the input noise.
class_docstr_tok: ["A","class","for","defining","parameter","distributions",".","Intended","for","robust","optimization","use","cases",".","This","could","be","used","to","specify","the","distribution","of","an","environmental","variable","or","the","distribution","of","the","input","noise","."]
docstr: Check if the parameters are environmental variables of the given
  search space.
  
  Args:
      search_space: The search space to check.
  
  Returns:
      A boolean denoting whether the parameters are environmental variables.
docstr_tok: ["Check","if","the","parameters","are","environmental","variables","of","the","given","search","space",".","Args",":","search_space",":","The","search","space","to","check",".","Returns",":","A","boolean","denoting","whether","the","parameters","are","environmental","variables","."]
code: def is_environmental(self, search_space: RobustSearchSpace) -> bool:
      r"""Check if the parameters are environmental variables of the given
      search space.
  
      Args:
          search_space: The search space to check.
  
      Returns:
          A boolean denoting whether the parameters are environmental variables.
      """
      return any(
          search_space.is_environmental_variable(p)
          for p in self.parameters
      )
  
file_path: Ax/ax/core/parameter_distribution.py
imports: from __future__ import annotations
  from copy import deepcopy
  from importlib import import_module
  from typing import Any, Dict, List, Optional, TYPE_CHECKING
  from ax.exceptions.core import UserInputError
  from ax.utils.common.base import SortableBase
label_desc: Use image node_id 8 for calling the ParameterDistribution obj's underlying member method code with example usage: obj.is_environmental(search_space) and returns: any