这可能是因为您在BeamRunPythonPipelineOperator任务中使用的Python环境与您的Airflow环境不兼容或不同。您可以尝试将BeamRunPythonPipelineOperator所使用的Python3.x环境转移到您的Airflow环境中。

以下是代码示例，展示如何定义并转移BeamRunPythonPipelineOperator所使用的Python环境：

from datetime import datetime
from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from airflow.contrib.operators.dataflow_operator import DataFlowPythonOperator
from google.cloud import dataflow

default_args = {
    'owner': 'airflow',
    'depends_on_past': False,
    'start_date': datetime(2021, 1, 1),
    'email': ['airflow@example.com'],
    'email_on_failure': False,
    'email_on_retry': False
}

dag = DAG('beam_python_operator_example_dag', default_args=default_args, schedule_interval='@once')

# 定义要在BeamRunPythonPipelineOperator中使用的Python环境
beam_pipeline_env = {
    'test_env': 'gcp-dataflow-py37',
    'options': {
        'project': 'my-gcp-project',
        'region': 'my-gcp-region'
    },
    'py_requirements': 'requirements.txt'
}

def run_beam_pipeline(**kwargs):
    """使用BeamRunPythonPipelineOperator运行某个Python文件"""
    options = kwargs['dag_run'].conf['dataflow_pipeline_options']
    p = dataflow.BeamPipelineRunner(**options)
    p.run()

with dag:
    # 转移Python3.x环境并使用DataFlowPythonOperator任务
    beam_pipeline_task = DataFlowPythonOperator(
        task_id='beam_python_task',
        py_file='/home/airflow/dataflow_scripts/my_beam_pipeline.py',
        default_args=default_args,
        dag=dag,
        gcp_conn_id='my_gcp_connection',
        dataflow_default_options=beam_pipeline_env
    )

    # 运行Beam管道
    run_beam_pipeline_task = PythonOperator(
        task_id='run_beam_pipeline_task',
        python_callable=run