code: def get_estimator_and_best_params(
      X,
      y,
      task: str,
      extended_task: str,
      step_config: Dict[str, Any],
      recipe_root: str,
      evaluation_metrics: Dict[str, RecipeMetric],
      primary_metric: str,
  ) -> Tuple["BaseEstimator", Dict[str, Any]]:
      return _create_model_automl(
          X,
          y,
          task,
          extended_task,
          step_config,
          recipe_root,
          evaluation_metrics,
          primary_metric,
      )
  
file_path: mlflow/mlflow/recipes/steps/automl/flaml.py
imports: import importlib
  import logging
  from typing import TYPE_CHECKING, Any, Dict, Tuple
  import pandas
  import mlflow
  from mlflow import MlflowException
  from mlflow.models import EvaluationMetric
  from mlflow.models.evaluation.default_evaluator import _get_binary_classifier_metrics, _get_regressor_metrics
  from mlflow.recipes.utils.metrics import RecipeMetric, _load_custom_metrics
label_desc: Use image node_id 1 for calling a global function with example usage: get_estimator_and_best_params(X, y, task, extended_task, step_config, recipe_root, evaluation_metrics, primary_metric) and returns: _create_model_automl