class_docstr: Represents an input example for MLflow model.
  
  Contains jsonable data that can be saved with the model and meta data about the exported format
  that can be saved with :py:class:`Model <mlflow.models.Model>`.
  
  The _Example is created from example data provided by user. The example(s) can be provided as
  pandas.DataFrame, numpy.ndarray, python dictionary or python list. The assumption is that the
  example contains jsonable elements (see storage format section below).
  
  NOTE: If the example is 1 dimensional (e.g. dictionary of str -> scalar, or a list of scalars),
  the assumption is that it is a single column of data.
  
  Metadata:
  
  The _Example metadata contains the following information:
      - artifact_path: Relative path to the serialized example within the model directory.
      - type: Type of example data provided by the user. E.g. dataframe, ndarray.
      - One of the following metadata based on the `type`:
          - pandas_orient: For dataframes, this attribute specifies how is the dataframe encoded
                           in json. For example, "split" value signals that the data is stored as
                           object with columns and data attributes.
          - format: For tensors, this attribute specifies the standard being used to store an
                    input example. MLflow uses a JSON-formatted string representation of T
                    F serving input.
  
  Storage Format:
  
  The examples are stored as json for portability and readability. Therefore, the contents of the
  example(s) must be jsonable. MLflow will make the following conversions automatically on behalf
  of the user:
  
      - binary values: :py:class:`bytes` or :py:class:`bytearray` are converted to base64
        encoded strings.
      - numpy types: Numpy types are converted to the corresponding python types or their closest
        equivalent.
      - csc/csr matrix: similar to 2 dims numpy array, csc/csr matrix are converted to
        corresponding python types or their closest equivalent.
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docstr: Returns the params dictionary that PyFunc wrapped models can use for scoring.
docstr_tok: ["Returns","the","params","dictionary","that","PyFunc","wrapped","models","can","use","for","scoring","."]
code: def inference_params(self):
      """
      Returns the params dictionary that PyFunc wrapped models can use for scoring.
      """
      return self._inference_params
  
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 4 for calling the _Example obj's underlying member method code with example usage: obj.inference_params() and returns: self