code: def deserialize_request(form) -> tuple:
      """This function will deserialize the REST API request packet to create a pandas dataframe that is input to the
      Ludwig predict method and a list of files that will be cleaned up at the end of processing.
  
      Args:
          form: REST API provide form data
  
      Returns: tuple(pandas.DataFrame, list of temporary files to clean up)
      """
      files = []
      file_index = {}
      for k, v in form.multi_items():
          if type(v) == UploadFile:
              file_index[v.filename] = _write_file(v, files)
  
      # reconstruct the dataframe
      df = deserialize_payload(form["payload"])
  
      # insert files paths of the temporary files in place of the original
      # file paths specified by the user.
      # pd.DataFrame.replace() method is used to replace file path string
      # specified by the user context with the file path string where a
      # temporary file containing the same content.
      # parameters for replace() method:
      #   to_replace: list of file path strings that the user provided
      #   value: list of temporary files created for each input file
      #
      # IMPORTANT: There is a one-to-one correspondence of the to_replace list
      # and the value list. Each list must be the same size.
      df.replace(
          to_replace=list(file_index.keys()),
          value=list(file_index.values()),
          inplace=True,
      )
  
      return df, files
  
file_path: ludwig/ludwig/utils/server_utils.py
imports: import json
  import os
  import tempfile
  from typing import Any, Dict, Union
  import numpy
  import pandas
  from starlette.datastructures import UploadFile
  from starlette.responses import JSONResponse
  from ludwig.utils.data_utils import NumpyEncoder
label_desc: Use image node_id 4 for calling a global function with example usage: deserialize_request(form) and returns: df,  files