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 freeze(self, sample: dict[str, Any]) -> GraphModelSpace:
      assert (
          self.model is not None
      ), "Mutator must be bound to a model before freezing."
      model = self.model
      for mutator in self.mutables:
          with mutator.bind_model(model):
              model = mutator.freeze(sample)
      return model
  
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 4 for calling the MutatorSequence obj's underlying member method code with example usage: obj.freeze(sample) and returns: model