class_docstr: In a preference Experiment (i.e. Metric values may either be zero for No and
  nonzero for Yes) do not transition until a minimum number of both Yes and No
  responses have been received.
class_docstr_tok: ["In","a","preference","Experiment","(","i.e",".","Metric","values","may","either","be","zero","for","No","and","nonzero","for","Yes",")","do","not","transition","until","a","minimum","number","of","both","Yes","and","No","responses","have","been","received","."]
code: def block_continued_generation_error(
      self,
      node_name: Optional[str],
      model_name: Optional[str],
      experiment: Optional[Experiment],
      trials_from_node: Optional[Set[int]] = None,
  ) -> None:
      pass
  
imports: from abc import abstractmethod
  from logging import Logger
  from typing import List, Optional, Set
  from ax.core.base_trial import TrialStatus
  from ax.core.experiment import Experiment
  from ax.exceptions.generation_strategy import MaxParallelismReachedException
  from ax.modelbridge.generation_strategy import DataRequiredError
  from ax.utils.common.base import SortableBase
  from ax.utils.common.logger import get_logger
  from ax.utils.common.serialization import SerializationMixin, serialize_init_args
label_desc: Use image node_id 3 for calling the MinimumPreferenceOccurances obj's underlying member method code with example usage: obj.block_continued_generation_error(node_name, model_name, experiment, trials_from_node) without return types