要在Airflow中实现从云函数接收数据，你可以使用Google Cloud Operator来创建一个云函数，然后使用Google Cloud Function Sensor来检测云函数是否返回数据。

以下是一个示例代码：

from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from airflow.operators.sensors import BaseSensorOperator
from airflow.providers.google.cloud.operators.functions import TriggerFunctionExecutionOperator
from airflow.providers.google.cloud.sensors.functions import GoogleCloudFunctionWaitingForFunctionExecutionSensor
from datetime import datetime

default_args = {
    'owner': 'airflow',
    'depends_on_past': False,
    'start_date': datetime(2021, 1, 1),
    'email_on_failure': False,
    'email_on_retry': False,
    'retries': 1,
    'retry_delay': timedelta(minutes=5),
}

with DAG('cloud_function_dag', default_args=default_args, schedule_interval=None) as dag:

    create_function = TriggerFunctionExecutionOperator(
        task_id='create_function',
        project_id='project_id',
        location='location_name',
        function_id='function_name',
        data='{"param1": "value1"}',
        trigger_resource_id='cloud_function_trigger',
        gcp_conn_id='google_cloud_default',
    )

    def process_data(**kwargs):
        data = kwargs['task_instance'].xcom_pull(task_ids='waiting_for_data')
        # Do some processing with data

    waiting_for_data = GoogleCloudFunctionWaitingForFunctionExecutionSensor(
        task_id='waiting_for_data',
        project_id='project_id',
        location='location_name',
        function_id='function_name',
        gcp_conn_id='google_cloud_default',
    )

    process_data = PythonOperator(
        task_id='process_data',
        python_callable=process_data,
        provide_context=True,
    )

    create_function >> waiting_for_data >> process_data

在这个例子中，我们创建了一个云函数并向其传递了一些数据。我们然后使用Google Cloud Function Waiting For Function Execution Sensor来检测该函数是否已经执行并返回了数据。如果数据已返回，我们将通过Python Operator来对数据进行处理。