class_docstr: Specification (aka space / range / domain) of one single parameter.
  
  NOTE: For `loguniform` (and `qloguniform`), the fields `low` and `high` are logarithm of original values.
class_docstr_tok: ["Specification","(","aka","space","\/","range","\/","domain",")","of","one","single","parameter",".","NOTE",":","For","`","loguniform","`","(","and","`","qloguniform","`",")",",","the","fields","`","low","`","and","`","high","`","are","logarithm","of","original","values","."]
docstr: For nested search space, check whether this parameter should be skipped for current set of paremters.
  This function must be used in a pattern similar to random tuner. Otherwise it will misbehave.
docstr_tok: ["For","nested","search","space",",","check","whether","this","parameter","should","be","skipped","for","current","set","of","paremters",".","This","function","must","be","used","in","a","pattern","similar","to","random","tuner",".","Otherwise","it","will","misbehave","."]
code: def is_activated_in(
      self, partial_parameters: FormattedParameters
  ) -> bool:
      """
      For nested search space, check whether this parameter should be skipped for current set of paremters.
      This function must be used in a pattern similar to random tuner. Otherwise it will misbehave.
      """
      if self.is_nested():
          return partial_parameters[self.key[:-2]] == self.key[-2]
      else:
          return True
  
file_path: nni/nni/common/hpo_utils/formatting.py
imports: from __future__ import annotations
  import math
  from types import SimpleNamespace
  from typing import Any, Dict, NamedTuple, Tuple, cast
  import numpy
  from nni.typehint import Parameters, SearchSpace
label_desc: Use image node_id 1 for calling the ParameterSpec obj's underlying member method code with example usage: obj.is_activated_in(partial_parameters) and returns: unknown, True