code: def _validate_and_wrap_lc_model(lc_model, loader_fn):
      import langchain.agents.agent
      import langchain.chains.base
      import langchain.chains.llm
      import langchain.llms.huggingface_hub
      import langchain.llms.openai
      import langchain.schema
  
      if not isinstance(lc_model, supported_lc_types()):
          raise mlflow.MlflowException.invalid_parameter_value(
              _UNSUPPORTED_MODEL_ERROR_MESSAGE.format(
                  instance_type=type(lc_model).__name__
              )
          )
  
      _SUPPORTED_LLMS = _get_supported_llms()
      if isinstance(
          lc_model, langchain.chains.llm.LLMChain
      ) and not any(
          isinstance(lc_model.llm, supported_llm)
          for supported_llm in _SUPPORTED_LLMS
      ):
          logger.warning(
              _UNSUPPORTED_LLM_WARNING_MESSAGE,
              type(lc_model.llm).__name__,
          )
  
      if isinstance(
          lc_model, langchain.agents.agent.AgentExecutor
      ) and not any(
          isinstance(lc_model.agent.llm_chain.llm, supported_llm)
          for supported_llm in _SUPPORTED_LLMS
      ):
          logger.warning(
              _UNSUPPORTED_LLM_WARNING_MESSAGE,
              type(lc_model.agent.llm_chain.llm).__name__,
          )
  
      if special_chain_info := _get_special_chain_info_or_none(
          lc_model
      ):
          if isinstance(
              lc_model, langchain.chains.RetrievalQA
          ) and version.parse(langchain.__version__) < version.parse(
              "0.0.194"
          ):
              raise mlflow.MlflowException.invalid_parameter_value(
                  _UNSUPPORTED_LANGCHAIN_VERSION_ERROR_MESSAGE.format(
                      instance_type=type(lc_model).__name__
                  )
              )
          if loader_fn is None:
              raise mlflow.MlflowException.invalid_parameter_value(
                  f"For {type(lc_model).__name__} models, a `loader_fn` must be provided."
              )
          if not isinstance(loader_fn, types.FunctionType):
              raise mlflow.MlflowException.invalid_parameter_value(
                  "The `loader_fn` must be a function that returns a {loader_arg}.".format(
                      loader_arg=special_chain_info.loader_arg
                  )
              )
  
      # If lc_model is a retriever, wrap it in a _RetrieverChain
      if isinstance(lc_model, langchain.schema.BaseRetriever):
          from mlflow.langchain.retriever_chain import _RetrieverChain
  
          if loader_fn is None:
              raise mlflow.MlflowException.invalid_parameter_value(
                  f"For {type(lc_model).__name__} models, a `loader_fn` must be provided."
              )
          if not isinstance(loader_fn, types.FunctionType):
              raise mlflow.MlflowException.invalid_parameter_value(
                  "The `loader_fn` must be a function that returns a retriever."
              )
          lc_model = _RetrieverChain(retriever=lc_model)
  
      return lc_model
  
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 12 for calling a global function with example usage: _validate_and_wrap_lc_model(lc_model, loader_fn) and returns: lc_model