class_docstr: The metadata of a logged MLflow Model.
class_docstr_tok: ["The","metadata","of","a","logged","MLflow","Model","."]
code: def __init__(
      self,
      artifact_path: str,
      flavors: Dict[str, Any],
      model_uri: str,
      model_uuid: str,
      run_id: str,
      saved_input_example_info: Optional[Dict[str, Any]],
      signature,  # Optional[ModelSignature]
      utc_time_created: str,
      mlflow_version: str,
      signature_dict: Optional[Dict[str, Any]] = None,
      metadata: Optional[Dict[str, Any]] = None,
  ):
      self._artifact_path = artifact_path
      self._flavors = flavors
      self._model_uri = model_uri
      self._model_uuid = model_uuid
      self._run_id = run_id
      self._saved_input_example_info = saved_input_example_info
      self._signature_dict = signature_dict
      self._signature = signature
      self._utc_time_created = utc_time_created
      self._mlflow_version = mlflow_version
      self._metadata = metadata
  
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 ModelInfo object  with example: obj = ModelInfo(artifact_path, flavors, model_uri, model_uuid, run_id, saved_input_example_info, signature, utc_time_created, mlflow_version, signature_dict, metadata)