class_docstr: An MLflow Model that can support multiple model flavors. Provides APIs for implementing
  new Model flavors.
class_docstr_tok: ["An","MLflow","Model","that","can","support","multiple","model","flavors",".","Provides","APIs","for","implementing","new","Model","flavors","."]
docstr: An optional definition of the expected inputs to and outputs from a model object, defined
  with both field names and data types. Signatures support both column-based and tensor-based
  inputs and outputs.
  
  :getter: Retrieves the signature of a model instance iff the model was saved with a
      signature definition.
  :setter: Sets a signature to a model instance.
  :type: Optional[ModelSignature]
docstr_tok: ["An","optional","definition","of","the","expected","inputs","to","and","outputs","from","a","model","object",",","defined","with","both","field","names","and","data","types",".","Signatures","support","both","column-based","and","tensor-based","inputs","and","outputs",".",":","getter",":","Retrieves","the","signature","of","a","model","instance","iff","the","model","was","saved","with","a","signature","definition",".",":","setter",":","Sets","a","signature","to","a","model","instance",".",":","type",":","Optional","[","ModelSignature","]"]
code: def signature(self):  # -> Optional[ModelSignature]
      """
      An optional definition of the expected inputs to and outputs from a model object, defined
      with both field names and data types. Signatures support both column-based and tensor-based
      inputs and outputs.
  
      :getter: Retrieves the signature of a model instance iff the model was saved with a
          signature definition.
      :setter: Sets a signature to a model instance.
      :type: Optional[ModelSignature]
      """
      return self._signature
  
imports: import json
  import logging
  import os
  import uuid
  import warnings
  from datetime import datetime
  from typing import Any, Callable, Dict, Optional, Union
  import yaml
  import mlflow
  from mlflow.artifacts import download_artifacts
  from mlflow.exceptions import MlflowException
  from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS
  from mlflow.tracking._tracking_service.utils import _resolve_tracking_uri
  from mlflow.utils.annotations import experimental
  from mlflow.utils.databricks_utils import get_databricks_runtime
  from mlflow.utils.file_utils import TempDir
  from mlflow.utils.uri import get_uri_scheme
label_desc: Use image node_id 11 for calling the Model obj's underlying member method code with example usage: obj.signature() and returns: self