code: def from_recipe_config(cls, recipe_config, recipe_root):
      step_config = {}
      if recipe_config.get("steps", {}).get("predict", {}) is not None:
          step_config.update(
              recipe_config.get("steps", {}).get("predict", {})
          )
      if (
          recipe_config.get("steps", {})
          .get("predict", {})
          .get("output", {})
          is not None
      ):
          step_config.update(
              recipe_config.get("steps", {})
              .get("predict", {})
              .get("output", {})
          )
      step_config["register"] = recipe_config.get("steps", {}).get(
          "register", {}
      )
      step_config["model_registry"] = recipe_config.get(
          "model_registry", {}
      )
      step_config["recipe"] = recipe_config.get(
          "recipe", "regression/v1"
      )
      if (
          recipe_config.get("model_registry", {}).get("registry_uri")
          is not None
      ):
          step_config["registry_uri"] = recipe_config.get(
              "model_registry", {}
          ).get("registry_uri")
  
      step_config.update(
          get_recipe_tracking_config(
              recipe_root_path=recipe_root,
              recipe_config=recipe_config,
          ).to_dict()
      )
      return cls(step_config, recipe_root)
  
imports: import logging
  import os
  import time
  from typing import Any, Dict
  import mlflow
  from mlflow.exceptions import BAD_REQUEST, INVALID_PARAMETER_VALUE, MlflowException
  from mlflow.recipes.artifacts import DataframeArtifact, RegisteredModelVersionInfo
  from mlflow.recipes.cards import BaseCard
  from mlflow.recipes.step import BaseStep, StepClass
  from mlflow.recipes.steps.register import _REGISTERED_MV_INFO_FILE
  from mlflow.recipes.utils.execution import get_step_output_path
  from mlflow.recipes.utils.step import get_pandas_data_profiles
  from mlflow.recipes.utils.tracking import TrackingConfig, apply_recipe_tracking_config, get_recipe_tracking_config
  from mlflow.utils._spark_utils import _create_local_spark_session_for_recipes, _get_active_spark_session
  from mlflow.utils.databricks_utils import get_databricks_env_vars
  from mlflow.utils.file_utils import write_spark_dataframe_to_parquet_on_local_disk
label_desc: Use image node_id 5 for calling the PredictStep obj's underlying member method code with example usage: obj.from_recipe_config(cls, recipe_config, recipe_root) and returns: cls