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: Make the class hashable to support the use of `lru_cache` above.
  
  NOTE: The hash of two `ParameterDistribution`s with identical attributes
  will be the same. This is compatible with the use in `lru_cache` above,
  since the resulting distributions will be the same.
docstr_tok: ["Make","the","class","hashable","to","support","the","use","of","`","lru_cache","`","above",".","NOTE",":","The","hash","of","two","`","ParameterDistribution","`","s","with","identical","attributes","will","be","the","same",".","This","is","compatible","with","the","use","in","`","lru_cache","`","above",",","since","the","resulting","distributions","will","be","the","same","."]
code: def __hash__(self) -> int:
      """Make the class hashable to support the use of `lru_cache` above.
  
      NOTE: The hash of two `ParameterDistribution`s with identical attributes
      will be the same. This is compatible with the use in `lru_cache` above,
      since the resulting distributions will be the same.
      """
      return hash(repr(self))
  
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 10 for calling the ParameterDistribution obj's underlying member method code with example usage: obj.__hash__() and returns: hash