code: def _get_mlflow_model_input_example_dict(
      mlflow_model: Model, path: str
  ):
      """
      Read input_example dictionary from the model artifact path. Returns None if there is no
      example metadata.
      :param mlflow_model: Model metadata.
      :param path: Path to the model directory.
      :return: Input example or None if the model has no example.
      """
      if mlflow_model.saved_input_example_info is None:
          return None
      example_type = mlflow_model.saved_input_example_info["type"]
      if example_type not in [
          "dataframe",
          "ndarray",
          "sparse_matrix_csc",
          "sparse_matrix_csr",
          "json_object",
      ]:
          raise MlflowException(
              f"This version of mlflow can not load example of type {example_type}"
          )
      path = os.path.join(
          path, mlflow_model.saved_input_example_info["artifact_path"]
      )
      with open(path) as handle:
          return json.load(handle)
  
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 3 for calling a global function with example usage: _get_mlflow_model_input_example_dict(mlflow_model, path) and returns: None, json