code: def run_pipeline_from_automl(
      train_file_path: str,
      test_file_path: str,
      max_run_time: timedelta = timedelta(minutes=10),
  ):
      """Function run pipeline with Auto ML models in nodes
  
      :param train_file_path: path to the csv file with data for train
      :param test_file_path: path to the csv file with data for validation
      :param max_run_time: maximum running time for customization of the "tpot" model
  
      :return roc_auc_value: ROC AUC metric for pipeline
      """
      with OperationTypesRepository.init_automl_repository() as _:
          train_data = InputData.from_csv(train_file_path)
          test_data = InputData.from_csv(test_file_path)
  
          testing_target = test_data.target
  
          node_scaling = PipelineNode("scaling")
          node_tpot = PipelineNode("tpot_class")
  
          node_tpot.parameters = {"timeout": max_run_time.seconds}
  
          node_lda = PipelineNode("lda", nodes_from=[node_scaling])
          node_rf = PipelineNode("rf", nodes_from=[node_tpot, node_lda])
          pipeline = Pipeline(node_rf)
  
          pipeline.fit(train_data)
          results = pipeline.predict(test_data)
  
          roc_auc_value = roc_auc(
              y_true=testing_target, y_score=results.predict
          )
          print(roc_auc_value)
      return roc_auc_value
  
file_path: FEDOT/examples/advanced/automl/pipeline_from_automl.py
imports: from datetime import timedelta
  from fedot.core.repository.operation_types_repository import OperationTypesRepository
  from sklearn.metrics import roc_auc_score
  from cases.data.data_utils import get_scoring_case_data_paths
  from fedot.core.data.data import InputData
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
label_desc: Use image node_id 1 for calling a global function with example usage: run_pipeline_from_automl(train_file_path, test_file_path, max_run_time) and returns: roc_auc_value