class_docstr: Checks the status of a pipeline run.
  
  :param azure_data_factory_conn_id: The connection identifier for connecting to Azure Data Factory.
  :param run_id: The pipeline run identifier.
  :param resource_group_name: The resource group name.
  :param factory_name: The data factory name.
  :param deferrable: Run sensor in the deferrable mode.
docstr: Create and return an AzureDataFactoryHook (cached).
docstr_tok: ["Create","and","return","an","AzureDataFactoryHook","(","cached",")","."]
code: def hook(self):
      """Create and return an AzureDataFactoryHook (cached)."""
      return AzureDataFactoryHook(
          azure_data_factory_conn_id=self.azure_data_factory_conn_id
      )
  
file_path: airflow/airflow/providers/microsoft/azure/sensors/data_factory.py
imports: from __future__ import annotations
  from datetime import timedelta
  from functools import cached_property
  from typing import TYPE_CHECKING, Sequence
  from airflow.configuration import conf
  from airflow.exceptions import AirflowException, AirflowSkipException
  from airflow.providers.microsoft.azure.hooks.data_factory import AzureDataFactoryHook, AzureDataFactoryPipelineRunException, AzureDataFactoryPipelineRunStatus
  from airflow.providers.microsoft.azure.triggers.data_factory import ADFPipelineRunStatusSensorTrigger
  from airflow.sensors.base import BaseSensorOperator
label_desc: Use image node_id 2 for calling the AzureDataFactoryPipelineRunStatusSensor obj's underlying member method code with example usage: obj.hook() and returns: AzureDataFactoryHook