code: def _save_base_lcs(model, path, loader_fn=None, persist_dir=None):
      import langchain.agents.agent
      import langchain.chains.base
      import langchain.chains.llm
  
      model_data_path = os.path.join(path, _MODEL_DATA_YAML_FILE_NAME)
      model_data_kwargs = {
          _MODEL_DATA_KEY: _MODEL_DATA_YAML_FILE_NAME,
          _MODEL_LOAD_KEY: _BASE_LOAD_KEY,
      }
  
      if isinstance(model, langchain.chains.llm.LLMChain):
          model.save(model_data_path)
      elif isinstance(model, langchain.agents.agent.AgentExecutor):
          if model.agent and model.agent.llm_chain:
              model.agent.llm_chain.save(model_data_path)
  
          if model.agent:
              agent_data_path = os.path.join(
                  path, _AGENT_DATA_FILE_NAME
              )
              model.save_agent(agent_data_path)
              model_data_kwargs[_AGENT_DATA_KEY] = _AGENT_DATA_FILE_NAME
  
          if model.tools:
              tools_data_path = os.path.join(
                  path, _TOOLS_DATA_FILE_NAME
              )
              try:
                  with open(tools_data_path, "wb") as f:
                      cloudpickle.dump(model.tools, f)
              except Exception as e:
                  raise mlflow.MlflowException(
                      "Error when attempting to pickle the AgentExecutor tools. "
                      "This model likely does not support serialization."
                  ) from e
              model_data_kwargs[_TOOLS_DATA_KEY] = _TOOLS_DATA_FILE_NAME
          else:
              raise mlflow.MlflowException.invalid_parameter_value(
                  "For initializing the AgentExecutor, tools must be provided."
              )
  
          key_to_ignore = [
              "llm_chain",
              "agent",
              "tools",
              "callback_manager",
          ]
          temp_dict = {
              k: v
              for k, v in model.__dict__.items()
              if k not in key_to_ignore
          }
  
          agent_primitive_path = os.path.join(
              path, _AGENT_PRIMITIVES_FILE_NAME
          )
          with open(agent_primitive_path, "w") as config_file:
              json.dump(temp_dict, config_file, indent=4)
  
          model_data_kwargs[
              _AGENT_PRIMITIVES_DATA_KEY
          ] = _AGENT_PRIMITIVES_FILE_NAME
  
      elif special_chain_info := _get_special_chain_info_or_none(model):
          # Save loader_fn by pickling
          loader_fn_path = os.path.join(path, _LOADER_FN_FILE_NAME)
          with open(loader_fn_path, "wb") as f:
              cloudpickle.dump(loader_fn, f)
          model_data_kwargs[_LOADER_FN_KEY] = _LOADER_FN_FILE_NAME
          model_data_kwargs[
              _LOADER_ARG_KEY
          ] = special_chain_info.loader_arg
  
          if persist_dir is not None:
              if os.path.exists(persist_dir):
                  # Save persist_dir by copying into subdir _PERSIST_DIR_NAME
                  persist_dir_data_path = os.path.join(
                      path, _PERSIST_DIR_NAME
                  )
                  shutil.copytree(persist_dir, persist_dir_data_path)
                  model_data_kwargs[
                      _PERSIST_DIR_KEY
                  ] = _PERSIST_DIR_NAME
              else:
                  raise mlflow.MlflowException.invalid_parameter_value(
                      "The directory provided for persist_dir does not exist."
                  )
  
          # Save model
          model.save(model_data_path)
      elif isinstance(model, langchain.chains.base.Chain):
          logger.warning(
              _UNSUPPORTED_MODEL_WARNING_MESSAGE,
              type(model).__name__,
          )
          model.save(model_data_path)
      else:
          raise mlflow.MlflowException.invalid_parameter_value(
              _UNSUPPORTED_MODEL_ERROR_MESSAGE.format(
                  instance_type=type(model).__name__
              )
          )
  
      return model_data_kwargs
  
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 13 for calling a global function with example usage: _save_base_lcs(model, path, loader_fn, persist_dir) and returns: model_data_kwargs