class_docstr: A mutator that can be dry run.
  
  :class:`StationaryMutator` invoke :class:`StationaryMutator.dry_run` to predict choice candidates,
  such that the mutator simplifies to some static choices within `simplify()`.
  This could be convenient to certain algorithms which do not want to handle dynamic samplers.
class_docstr_tok: ["A","mutator","that","can","be","dry","run",".",":","class",":","`","StationaryMutator","`","invoke",":","class",":","`","StationaryMutator.dry_run","`","to","predict","choice","candidates",",","such","that","the","mutator","simplifies","to","some","static","choices","within","`","simplify","(",")","`",".","This","could","be","convenient","to","certain","algorithms","which","do","not","want","to","handle","dynamic","samplers","."]
code: def __init__(
      self,
      *,
      sampler: Optional[MutationSampler] = None,
      label: Optional[str] = None,
  ):
      super().__init__(sampler=sampler, label=label)
      self._dry_run_choices: Optional[MutableDict] = None
  
imports: from __future__ import annotations
  from contextlib import contextmanager
  from typing import Any, Callable, Iterable, List, Optional, TYPE_CHECKING, Iterator
  from numpy.random import RandomState
  from nni.mutable import LabeledMutable, MutableList, MutableDict, Categorical, Mutable, SampleValidationError, Sample, SampleMissingError, label_scope, auto_label, frozen_context
  from .space import ModelStatus
label_desc: Use image node_id 1 to create a new StationaryMutator object from inherited base classes: Mutator with example: obj = StationaryMutator()