class_docstr: Poll the pipeline until it reaches a terminal state; raise AirflowException with the failure reason.
  
  .. seealso::
      For more information on how to use this sensor, take a look at the guide:
      :ref:`howto/sensor:SageMakerPipelineSensor`
  
  :param pipeline_exec_arn: ARN of the pipeline to watch.
  :param verbose: Whether to print steps details while waiting for completion.
          Defaults to true, consider turning off for pipelines that have thousands of steps.
class_docstr_tok: ["Poll","the","pipeline","until","it","reaches","a","terminal","state",";","raise","AirflowException","with","the","failure","reason",".","..","seealso",":",":","For","more","information","on","how","to","use","this","sensor",",","take","a","look","at","the","guide",":",":","ref",":","`","howto\/sensor",":","SageMakerPipelineSensor","`",":","param","pipeline_exec_arn",":","ARN","of","the","pipeline","to","watch",".",":","param","verbose",":","Whether","to","print","steps","details","while","waiting","for","completion",".","Defaults","to","true",",","consider","turning","off","for","pipelines","that","have","thousands","of","steps","."]
code: def __init__(
      self, *, pipeline_exec_arn: str, verbose: bool = True, **kwargs
  ):
      super().__init__(resource_type="pipeline", **kwargs)
      self.pipeline_exec_arn = pipeline_exec_arn
      self.verbose = verbose
  
imports: from __future__ import annotations
  import time
  from functools import cached_property
  from typing import TYPE_CHECKING, Sequence
  from deprecated import deprecated
  from airflow.exceptions import AirflowException, AirflowProviderDeprecationWarning, AirflowSkipException
  from airflow.providers.amazon.aws.hooks.sagemaker import LogState, SageMakerHook
  from airflow.sensors.base import BaseSensorOperator
label_desc: Use image node_id 1 to create a new SageMakerPipelineSensor object from inherited base classes: SageMakerBaseSensor with example: obj = SageMakerPipelineSensor()