class_docstr: Checks for the existence of a partition within a table in Google Bigquery.
  
  :param project_id: The Google cloud project in which to look for the table.
      The connection supplied to the hook must provide
      access to the specified project.
  :param dataset_id: The name of the dataset in which to look for the table.
      storage bucket.
  :param table_id: The name of the table to check the existence of.
  :param partition_id: The name of the partition to check the existence of.
  :param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
  :param impersonation_chain: Optional service account to impersonate using short-term
      credentials, or chained list of accounts required to get the access_token
      of the last account in the list, which will be impersonated in the request.
      If set as a string, the account must grant the originating account
      the Service Account Token Creator IAM role.
      If set as a sequence, the identities from the list must grant
      Service Account Token Creator IAM role to the directly preceding identity, with first
      account from the list granting this role to the originating account (templated).
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code: def poke(self, context: Context) -> bool:
      table_uri = f"{self.project_id}:{self.dataset_id}.{self.table_id}"
      self.log.info(
          'Sensor checks existence of partition: "%s" in table: %s',
          self.partition_id,
          table_uri,
      )
      hook = BigQueryHook(
          gcp_conn_id=self.gcp_conn_id,
          impersonation_chain=self.impersonation_chain,
      )
      return hook.table_partition_exists(
          project_id=self.project_id,
          dataset_id=self.dataset_id,
          table_id=self.table_id,
          partition_id=self.partition_id,
      )
  
imports: from __future__ import annotations
  import warnings
  from datetime import timedelta
  from typing import TYPE_CHECKING, Any, Sequence
  from airflow.configuration import conf
  from airflow.exceptions import AirflowException, AirflowProviderDeprecationWarning, AirflowSkipException
  from airflow.providers.google.cloud.hooks.bigquery import BigQueryHook
  from airflow.providers.google.cloud.triggers.bigquery import BigQueryTableExistenceTrigger, BigQueryTablePartitionExistenceTrigger
  from airflow.sensors.base import BaseSensorOperator
label_desc: Use image node_id 2 for calling the BigQueryTablePartitionExistenceSensor obj's underlying member method code with example usage: obj.poke(context) and returns: hook