class_docstr: A wrapper class that should be used for testing purposes only.
class_docstr_tok: ["A","wrapper","class","that","should","be","used","for","testing","purposes","only","."]
docstr: Model input data and additional parameters.
  
  Args:
      data: Model input data.
      params: Additional parameters to pass to the model for inference.
  
          .. Note:: Experimental: This parameter may change or be removed in a future
              release without warning.
  
  Returns:
      Model predictions.
docstr_tok: ["Model","input","data","and","additional","parameters",".","Args",":","data",":","Model","input","data",".","params",":","Additional","parameters","to","pass","to","the","model","for","inference",".","..","Note",":",":","Experimental",":","This","parameter","may","change","or","be","removed","in","a","future","release","without","warning",".","Returns",":","Model","predictions","."]
code: def predict(
      self,
      data,
      params: Optional[
          Dict[str, Any]
      ] = None,  # pylint: disable=unused-argument
  ):
      """
      Model input data and additional parameters.
  
      Args:
          data: Model input data.
          params: Additional parameters to pass to the model for inference.
  
              .. Note:: Experimental: This parameter may change or be removed in a future
                  release without warning.
  
      Returns:
          Model predictions.
      """
      import langchain
      from langchain.schema.retriever import BaseRetriever
  
      from mlflow.utils.openai_utils import (
          TEST_CONTENT,
          TEST_INTERMEDIATE_STEPS,
          TEST_SOURCE_DOCUMENTS,
      )
  
      from tests.langchain.test_langchain_model_export import (
          _mock_async_request,
      )
  
      if isinstance(
          self.lc_model,
          (
              langchain.chains.llm.LLMChain,
              langchain.chains.RetrievalQA,
              BaseRetriever,
          ),
      ):
          mockContent = TEST_CONTENT
      elif isinstance(
          self.lc_model, langchain.agents.agent.AgentExecutor
      ):
          mockContent = f"Final Answer: {TEST_CONTENT}"
      else:
          mockContent = TEST_CONTENT
  
      with _mock_async_request(mockContent):
          result = super().predict(data)
      if (
          hasattr(self.lc_model, "return_source_documents")
          and self.lc_model.return_source_documents
      ):
          for res in result:
              res["source_documents"] = TEST_SOURCE_DOCUMENTS
      if (
          hasattr(self.lc_model, "return_intermediate_steps")
          and self.lc_model.return_intermediate_steps
      ):
          for res in result:
              res["intermediate_steps"] = TEST_INTERMEDIATE_STEPS
  
      return result
  
imports: import logging
  import os
  from typing import Any, Dict, List, Optional, Union
  import pandas
  import yaml
  import mlflow
  from mlflow import pyfunc
  from mlflow.environment_variables import _MLFLOW_TESTING
  from mlflow.langchain.runnables import _load_runnables, _save_runnables
  from mlflow.langchain.utils import _BASE_LOAD_KEY, _MODEL_LOAD_KEY, _RUNNABLE_LOAD_KEY, _load_base_lcs, _save_base_lcs, _validate_and_wrap_lc_model, lc_runnables_types
  from mlflow.models import Model, ModelInputExample, ModelSignature
  from mlflow.models.model import MLMODEL_FILE_NAME
  from mlflow.models.signature import _infer_signature_from_input_example
  from mlflow.models.utils import _save_example
  from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS
  from mlflow.tracking.artifact_utils import _download_artifact_from_uri
  from mlflow.types.schema import ColSpec, DataType, Schema
  from mlflow.utils.annotations import experimental
  from mlflow.utils.docstring_utils import LOG_MODEL_PARAM_DOCS, format_docstring
  from mlflow.utils.environment import _CONDA_ENV_FILE_NAME, _CONSTRAINTS_FILE_NAME, _PYTHON_ENV_FILE_NAME, _REQUIREMENTS_FILE_NAME, _mlflow_conda_env, _process_conda_env, _process_pip_requirements, _PythonEnv, _validate_env_arguments
  from mlflow.utils.file_utils import get_total_file_size, write_to
  from mlflow.utils.model_utils import _add_code_from_conf_to_system_path, _get_flavor_configuration, _validate_and_copy_code_paths, _validate_and_prepare_target_save_path
  from mlflow.utils.requirements_utils import _get_pinned_requirement
label_desc: Use image node_id 1 for calling the _TestLangChainWrapper obj's underlying member method code with example usage: obj.predict(data, params) and returns: result