No I want to put them inside the pipeline.py file where I config all steps, like this:

from clearml import PipelineDecorator
from train_helpers.common import params
from dataset import DataModule
from train import Trainer


@PipelineDecorator.component(return_values=['_args'], cache=True)
def init_experiment():
    _args = params.parse_args()
    return _args


@PipelineDecorator.component(return_values=['data'], cache=False)
def data_preparation(args):
    data = DataModule(args)
    return data


@PipelineDecorator.component(cache=False)
def train_model(args, data):
    Trainer(args).train()


@PipelineDecorator.pipeline(name='Pipeline_decorator', project='Pipeline_decorator', version='0.1', pipeline_execution_queue=None)
def main():
    args, setup_logger = init_experiment()
    data = data_preparation(args)
    train_model(args, data)


if __name__ == '__main__':
    # PipelineDecorator.debug_pipeline()
    PipelineDecorator.run_locally()
    main()