在使用BeamRunPythonPipelineOperator时，需要配置DataflowBackend和project等参数，而且需要将参数传递给run_python_pipeline()方法来提交数据流作业。以下是一个简单的示例代码，演示如何使用BeamRunPythonPipelineOperator提交数据流作业：

from datetime import datetime
from airflow import DAG
from airflow.providers.google.cloud.operators.dataflow import BeamRunPythonPipelineOperator

default_args = {
    'start_date': datetime(2022, 1, 1),
    'catchup': False
}

with DAG(
        dag_id='example_beam_run_python_pipeline_operator',
        default_args=default_args,
        schedule_interval=None,
    ) as dag:
    
    def my_beam_pipeline():
        import apache_beam as beam
        from apache_beam.options.pipeline_options import PipelineOptions
        
        options = PipelineOptions.from_dictionary(runner='DataflowRunner',
                                                   project='my-project-id',
                                                   region='us-central1',
                                                   temp_location='gs://my-bucket/tmp')
        
        with beam.Pipeline(options=options) as p:
            pass  # Define your Beam pipeline here.
    
    submit_beam_job = BeamRunPythonPipelineOperator(
        task_id='submit_beam_job',
        py_file='./my_beam_pipeline.py',
        py_options=None,
        runner=None,
        gcp_conn_id=None,
        delegate_to=None,
        dataflow_default_options={
            'project': 'my-project-id',
            'region': 'us-central1',
            'temp_location': 'gs://my-bucket/tmp'
        }
    )
    
    submit_beam_job.run_python_pipeline(python_callable=my_beam_pipeline)

在上面的代码中，定义了一个名为my_beam_pipeline()的BEAM流水线作业。然后将其作为参数传递给run_python_pipeline()方法，并将DataflowBackend和project等参数传递给BeamRunPythonPipelineOperator。这将提交beam作业到Dataflow中，并在后台运行。