docstr: 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: ["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(  # pylint: disable=unused-argument
      self,
      data: Union[pd.DataFrame, List[Union[str, Dict[str, Any]]], Any],
      params: Optional[
          Dict[str, Any]
      ] = None,  # pylint: disable=unused-argument
  ) -> List[str]:
      """
      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.
      """
      messages = self._prepare_messages(data)
      from mlflow.langchain.api_request_parallel_processor import (
          process_api_requests,
      )
  
      return_first_element = isinstance(
          self.lc_model, lc_runnables_types()
      ) and not isinstance(data, pd.DataFrame)
      results = process_api_requests(
          lc_model=self.lc_model, requests=messages
      )
      return results[0] if return_first_element else results
  
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 2 for calling the _LangChainModelWrapper obj's underlying member method code with example usage: obj.predict(data, params) and returns: unknown