code: def _fake_mlflow_question_classifier():
      from langchain.callbacks.manager import CallbackManagerForLLMRun
      from langchain.chat_models.base import SimpleChatModel
      from langchain.schema.messages import BaseMessage
  
      class FakeMLflowClassifier(SimpleChatModel):
          """Fake Chat Model wrapper for testing purposes."""
  
          def _call(
              self,
              messages: List[BaseMessage],
              stop: Optional[List[str]] = None,
              run_manager: Optional[CallbackManagerForLLMRun] = None,
              **kwargs: Any,
          ) -> str:
              if "MLflow" in messages[0].content.split(":")[1]:
                  return "yes"
              if "cat" in messages[0].content.split(":")[1]:
                  return "no"
              return "unknown"
  
          @property
          def _llm_type(self) -> str:
              return "fake mlflow classifier"
  
      return FakeMLflowClassifier
  
imports: import json
  import logging
  import os
  import shutil
  import types
  from functools import lru_cache
  from importlib.util import find_spec
  from typing import Any, List, NamedTuple, Optional
  import cloudpickle
  import yaml
  from packaging import version
  import mlflow
  from mlflow.utils.class_utils import _get_class_from_string
label_desc: Use image node_id 20 for calling a global function with example usage: _fake_mlflow_question_classifier() and returns: FakeMLflowClassifier, str, str, str, str