code: def _read_tensor_input_from_json(path_or_data, schema=None):
      if isinstance(path_or_data, str) and os.path.exists(path_or_data):
          with open(path_or_data) as handle:
              inp_dict = json.load(handle)
      else:
          inp_dict = path_or_data
      return parse_tf_serving_input(inp_dict, schema)
  
imports: import datetime
  import decimal
  import json
  import logging
  import os
  from copy import deepcopy
  from typing import Any, Dict, List, Optional, Union
  import numpy
  import pandas
  from mlflow.exceptions import INVALID_PARAMETER_VALUE, MlflowException
  from mlflow.models import Model
  from mlflow.store.artifact.utils.models import get_model_name_and_version
  from mlflow.types import DataType, ParamSchema, ParamSpec, Schema, TensorSpec
  from mlflow.types.schema import Array, Object, Property
  from mlflow.types.utils import TensorsNotSupportedException, _infer_param_schema, clean_tensor_type
  from mlflow.utils.annotations import experimental
  from mlflow.utils.proto_json_utils import NumpyEncoder, dataframe_from_parsed_json, parse_tf_serving_input
  from mlflow.utils.uri import get_databricks_profile_uri_from_artifact_uri
label_desc: Use image node_id 6 for calling a global function with example usage: _read_tensor_input_from_json(path_or_data, schema) and returns: parse_tf_serving_input