code: def pipeline(data_url: str, project: str):
      dataset_id = create_dataset(
          source_url=data_url, project=project, dataset_name="mock"
      )
  
      preprocessed_dataset_id = preprocess_dataset(
          dataset_id=dataset_id
      )
  
      if not bool(
          verify_dataset_integrity(
              dataset_id=preprocessed_dataset_id,
              expected_num_columns=15,
          )
      ):
          print("Verification Failed!")
          return False
  
      print("start training models")
      models_score = []
      for i in [100, 150]:
          model_score = train_model(
              dataset_id=preprocessed_dataset_id,
              training_args=dict(num_boost_round=i),
          )
          models_score.append(model_score)
  
      model_id = select_best_model(models_score=models_score)
      print("selected model_id = {}".format(model_id))
  
imports: from clearml import PipelineDecorator, Task
label_desc: Use image node_id 6 for calling a global function with example usage: pipeline(data_url, project) and returns: False