code: def dataframe_from_raw_json(
      path_or_str, schema=None, pandas_orient: str = "split"
  ):
      """Parse raw json into a pandas.Dataframe.
  
      If schema is provided this methods will attempt to cast data types according to the schema. This
      include base64 decoding for binary columns.
  
      Args:
          path_or_str: Path to a json file or a json string.
          schema: MLflow schema used when parsing the data.
          pandas_orient: pandas data frame convention used to store the data.
  
      Returns:
          pandas.DataFrame.
      """
      if os.path.exists(path_or_str):
          with open(path_or_str) as f:
              parsed_json = json.load(f)
      else:
          parsed_json = json.loads(path_or_str)
  
      return dataframe_from_parsed_json(
          parsed_json, pandas_orient, schema
      )
  
imports: import base64
  import datetime
  import importlib
  import json
  import os
  from collections import defaultdict
  from copy import deepcopy
  from functools import partial
  from json import JSONEncoder
  from typing import Any, Dict, Optional
  from google.protobuf.descriptor import FieldDescriptor
  from google.protobuf.json_format import MessageToJson, ParseDict
  from mlflow.exceptions import MlflowException
  from mlflow.protos.databricks_pb2 import BAD_REQUEST
label_desc: Use image node_id 9 for calling a global function with example usage: dataframe_from_raw_json(path_or_str, schema, pandas_orient) and returns: dataframe_from_parsed_json