class_docstr: Wrapper class that creates a predict function such that
  predict(data: pd.DataFrame) -> model's output as pd.DataFrame (pandas DataFrame)
class_docstr_tok: ["Wrapper","class","that","creates","a","predict","function","such","that","predict","(","data",":","pd.DataFrame",")","-",">","model","'s","output","as","pd.DataFrame","(","pandas","DataFrame",")"]
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(
      self,
      data,
      params: Optional[
          Dict[str, Any]
      ] = None,  # pylint: disable=unused-argument
  ):
      """
      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 numpy as np
      import paddle
      import pandas as pd
  
      if isinstance(data, pd.DataFrame):
          inp_data = data.values.astype(np.float32)
      elif isinstance(data, np.ndarray):
          inp_data = data
      elif isinstance(data, (list, dict)):
          raise TypeError(
              "The paddle flavor does not support List or Dict input types. "
              "Please use a pandas.DataFrame or a numpy.ndarray"
          )
      else:
          raise TypeError(
              "Input data should be pandas.DataFrame or numpy.ndarray"
          )
      inp_data = np.squeeze(inp_data)
  
      self.pd_model.eval()
  
      predicted = self.pd_model(paddle.to_tensor(inp_data))
      return pd.DataFrame(predicted.numpy())
  
imports: import logging
  import os
  from typing import Any, Dict, Optional
  import yaml
  import mlflow
  from mlflow import pyfunc
  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.utils.autologging_utils import autologging_integration, safe_patch
  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 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 _PaddleWrapper obj's underlying member method code with example usage: obj.predict(data, params) and returns: pd