class_docstr: Apply a series of mutators on our model, sequentially.
  
  This could be generalized to a DAG indicating the dependencies between mutators,
  but we don't have a use case for that yet.
class_docstr_tok: ["Apply","a","series","of","mutators","on","our","model",",","sequentially",".","This","could","be","generalized","to","a","DAG","indicating","the","dependencies","between","mutators",",","but","we","do","n't","have","a","use","case","for","that","yet","."]
code: def __init__(self, mutators: list[Mutator]):
      assert all(
          isinstance(mutator, Mutator) for mutator in mutators
      ), "mutators must be a list of Mutator"
      super().__init__(mutators)
      self.model: Optional[GraphModelSpace] = 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 MutatorSequence object from inherited base classes: MutableList with example: obj = MutatorSequence(mutators)