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","."]
code: def __init__(
      self,
      artifact_path=None,
      run_id=None,
      utc_time_created=None,
      flavors=None,
      signature=None,  # ModelSignature
      saved_input_example_info: Optional[Dict[str, Any]] = None,
      model_uuid: Union[str, Callable, None] = lambda: uuid.uuid4().hex,
      mlflow_version: Union[str, None] = mlflow.version.VERSION,
      metadata: Optional[Dict[str, Any]] = None,
      model_size_bytes: Optional[int] = None,
      **kwargs,
  ):
      # store model id instead of run_id and path to avoid confusion when model gets exported
      if run_id:
          self.run_id = run_id
          self.artifact_path = artifact_path
  
      self.utc_time_created = str(utc_time_created or datetime.utcnow())
      self.flavors = flavors if flavors is not None else {}
      self.signature = signature
      self.saved_input_example_info = saved_input_example_info
      self.model_uuid = (
          model_uuid() if callable(model_uuid) else model_uuid
      )
      self.mlflow_version = mlflow_version
      self.metadata = metadata
      self.model_size_bytes = model_size_bytes
      self.__dict__.update(kwargs)
  
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 1 to create a new Model object  with example: obj = Model(artifact_path, run_id, utc_time_created, flavors, signature, saved_input_example_info, model_uuid, mlflow_version, metadata, model_size_bytes)