class_docstr: Trigger when the Azure data factory pipeline job finishes.
  
  When wait_for_termination is set to False, it triggers immediately with success status.
  
  :param run_id: Run id of a Azure data pipeline run job.
  :param azure_data_factory_conn_id: The connection identifier for connecting to Azure Data Factory.
  :param end_time: Time in seconds when triggers will timeout.
  :param resource_group_name: The resource group name.
  :param factory_name: The data factory name.
  :param wait_for_termination: Flag to wait on a pipeline run's termination.
  :param check_interval: Time in seconds to check on a pipeline run's status.
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code: def __init__(
      self,
      run_id: str,
      azure_data_factory_conn_id: str,
      end_time: float,
      resource_group_name: str,
      factory_name: str,
      wait_for_termination: bool = True,
      check_interval: int = 60,
  ):
      super().__init__()
      self.azure_data_factory_conn_id = azure_data_factory_conn_id
      self.check_interval = check_interval
      self.run_id = run_id
      self.wait_for_termination = wait_for_termination
      self.resource_group_name = resource_group_name
      self.factory_name = factory_name
      self.end_time = end_time
  
file_path: airflow/airflow/providers/microsoft/azure/triggers/data_factory.py
imports: from __future__ import annotations
  import asyncio
  import time
  from typing import Any, AsyncIterator
  from azure.core.exceptions import ServiceRequestError
  from airflow.providers.microsoft.azure.hooks.data_factory import AzureDataFactoryAsyncHook, AzureDataFactoryPipelineRunStatus
  from airflow.triggers.base import BaseTrigger, TriggerEvent
label_desc: Use image node_id 1 to create a new AzureDataFactoryTrigger object from inherited base classes: BaseTrigger with example: obj = AzureDataFactoryTrigger(run_id, azure_data_factory_conn_id, end_time, resource_group_name, factory_name, wait_for_termination, check_interval)