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 integer that represents the model size in bytes
  :getter: Retrieves the model size if it's calculated when the model is saved
  :setter: Sets the model size to a model instance
  :type: Optional[int]
docstr_tok: ["An","optional","integer","that","represents","the","model","size","in","bytes",":","getter",":","Retrieves","the","model","size","if","it","'s","calculated","when","the","model","is","saved",":","setter",":","Sets","the","model","size","to","a","model","instance",":","type",":","Optional","[","int","]"]
code: def model_size_bytes(self) -> Optional[int]:
      """
      An optional integer that represents the model size in bytes
      :getter: Retrieves the model size if it's calculated when the model is saved
      :setter: Sets the model size to a model instance
      :type: Optional[int]
      """
      return self._model_size_bytes
  
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 15 for calling the Model obj's underlying member method code with example usage: obj.model_size_bytes() and returns: self