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: Load a model from its YAML representation.
docstr_tok: ["Load","a","model","from","its","YAML","representation","."]
code: def from_dict(cls, model_dict):
      """Load a model from its YAML representation."""
  
      from mlflow.models.signature import ModelSignature
  
      model_dict = model_dict.copy()
      if "signature" in model_dict and isinstance(
          model_dict["signature"], dict
      ):
          model_dict["signature"] = ModelSignature.from_dict(
              model_dict["signature"]
          )
  
      if "model_uuid" not in model_dict:
          model_dict["model_uuid"] = None
  
      if _MLFLOW_VERSION_KEY not in model_dict:
          model_dict[_MLFLOW_VERSION_KEY] = None
  
      return cls(**model_dict)
  
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 24 for calling the Model obj's underlying member method code with example usage: obj.from_dict(cls, model_dict) and returns: cls