class_docstr: Loads files from Google Cloud Storage into BigQuery.
  
  The schema to be used for the BigQuery table may be specified in one of
  two ways. You may either directly pass the schema fields in, or you may
  point the operator to a Google Cloud Storage object name. The object in
  Google Cloud Storage must be a JSON file with the schema fields in it.
  
  .. seealso::
      For more information on how to use this operator, take a look at the guide:
      :ref:`howto/operator:GCSToBigQueryOperator`
  
  :param bucket: The bucket to load from. (templated)
  :param source_objects: String or List of Google Cloud Storage URIs to load from. (templated)
      If source_format is 'DATASTORE_BACKUP', the list must only contain a single URI.
  :param destination_project_dataset_table: The dotted
      ``(<project>.|<project>:)<dataset>.<table>`` BigQuery table to load data into.
      If ``<project>`` is not included, project will be the project defined in
      the connection json. (templated)
  :param schema_fields: If set, the schema field list as defined here:
      https://cloud.google.com/bigquery/docs/reference/v2/jobs#configuration.load
      Should not be set when source_format is 'DATASTORE_BACKUP'.
      Parameter must be defined if 'schema_object' is null and autodetect is False.
  :param schema_object: If set, a GCS object path pointing to a .json file that
      contains the schema for the table. (templated)
      Parameter must be defined if 'schema_fields' is null and autodetect is False.
  :param schema_object_bucket: [Optional] If set, the GCS bucket where the schema object
      template is stored. (templated) (Default: the value of ``bucket``)
  :param source_format: File format to export.
  :param compression: [Optional] The compression type of the data source.
      Possible values include GZIP and NONE.
      The default value is NONE.
      This setting is ignored for Google Cloud Bigtable,
      Google Cloud Datastore backups and Avro formats.
  :param create_disposition: The create disposition if the table doesn't exist.
  :param skip_leading_rows: The number of rows at the top of a CSV file that BigQuery
      will skip when loading the data.
      When autodetect is on, the behavior is the following:
      skip_leading_rows unspecified - Autodetect tries to detect headers in the first row.
      If they are not detected, the row is read as data. Otherwise, data is read starting
      from the second row.
      skip_leading_rows is 0 - Instructs autodetect that there are no headers and data
      should be read starting from the first row.
      skip_leading_rows = N > 0 - Autodetect skips N-1 rows and tries to detect headers
      in row N. If headers are not detected, row N is just skipped. Otherwise, row N is
      used to extract column names for the detected schema.
      Default value set to None so that autodetect option can detect schema fields.
  :param write_disposition: The write disposition if the table already exists.
  :param field_delimiter: The delimiter to use when loading from a CSV.
  :param max_bad_records: The maximum number of bad records that BigQuery can
      ignore when running the job.
  :param quote_character: The value that is used to quote data sections in a CSV file.
  :param ignore_unknown_values: [Optional] Indicates if BigQuery should allow
      extra values that are not represented in the table schema.
      If true, the extra values are ignored. If false, records with extra columns
      are treated as bad records, and if there are too many bad records, an
      invalid error is returned in the job result.
  :param allow_quoted_newlines: Whether to allow quoted newlines (true) or not (false).
  :param allow_jagged_rows: Accept rows that are missing trailing optional columns.
      The missing values are treated as nulls. If false, records with missing trailing
      columns are treated as bad records, and if there are too many bad records, an
      invalid error is returned in the job result. Only applicable to CSV, ignored
      for other formats.
  :param encoding: The character encoding of the data. See:
      https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs#configuration.query.tableDefinitions.(key).csvOptions.encoding
      https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#externalDataConfiguration.csvOptions.encoding
  :param max_id_key: If set, the name of a column in the BigQuery table
      that's to be loaded. This will be used to select the MAX value from
      BigQuery after the load occurs. The results will be returned by the
      execute() command, which in turn gets stored in XCom for future
      operators to use. This can be helpful with incremental loads--during
      future executions, you can pick up from the max ID.
  :param schema_update_options: Allows the schema of the destination
      table to be updated as a side effect of the load job.
  :param src_fmt_configs: configure optional fields specific to the source format
  :param external_table: Flag to specify if the destination table should be
      a BigQuery external table. Default Value is False.
  :param time_partitioning: configure optional time partitioning fields i.e.
      partition by field, type and  expiration as per API specifications.
      Note that 'field' is not available in concurrency with
      dataset.table$partition.
  :param cluster_fields: Request that the result of this load be stored sorted
      by one or more columns. BigQuery supports clustering for both partitioned and
      non-partitioned tables. The order of columns given determines the sort order.
      Not applicable for external tables.
  :param autodetect: [Optional] Indicates if we should automatically infer the
      options and schema for CSV and JSON sources. (Default: ``True``).
      Parameter must be set to True if 'schema_fields' and 'schema_object' are undefined.
      It is suggested to set to True if table are create outside of Airflow.
      If autodetect is None and no schema is provided (neither via schema_fields
      nor a schema_object), assume the table already exists.
  :param encryption_configuration: [Optional] Custom encryption configuration (e.g., Cloud KMS keys).
  
      .. code-block:: python
  
          encryption_configuration = {
              "kmsKeyName": "projects/testp/locations/us/keyRings/test-kr/cryptoKeys/test-key",
          }
  :param location: [Optional] The geographic location of the job. Required except for US and EU.
      See details at https://cloud.google.com/bigquery/docs/locations#specifying_your_location
  :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).
  :param labels: [Optional] Labels for the BiqQuery table.
  :param description: [Optional] Description for the BigQuery table. This will only be used if the
      destination table is newly created. If the table already exists and a value different than the
      current description is provided, the job will fail.
  :param deferrable: Run operator in the deferrable mode
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code: def execute(self, context: Context):
      hook = BigQueryHook(
          gcp_conn_id=self.gcp_conn_id,
          location=self.location,
          impersonation_chain=self.impersonation_chain,
      )
      self.hook = hook
      self.source_format = self.source_format.upper()
  
      job_id = self.hook.generate_job_id(
          job_id=self.job_id,
          dag_id=self.dag_id,
          task_id=self.task_id,
          logical_date=context["logical_date"],
          configuration=self.configuration,
          force_rerun=self.force_rerun,
      )
  
      self.source_objects = (
          self.source_objects
          if isinstance(self.source_objects, list)
          else [self.source_objects]
      )
      self.source_uris = [
          f"gs://{self.bucket}/{source_object}"
          for source_object in self.source_objects
      ]
  
      if not self.schema_fields:
          # Check for self.autodetect explicitly False. self.autodetect equal to None
          # entails we do not want to detect schema from files. Instead, it means we
          # rely on an already existing table's schema
          if not self.schema_object and self.autodetect is False:
              raise AirflowException(
                  "Table schema was not found. Neither schema object nor schema fields were specified"
              )
          if (
              self.schema_object
              and self.source_format != "DATASTORE_BACKUP"
          ):
              gcs_hook = GCSHook(
                  gcp_conn_id=self.gcp_conn_id,
                  impersonation_chain=self.impersonation_chain,
              )
              self.schema_fields = json.loads(
                  gcs_hook.download(
                      self.schema_object_bucket, self.schema_object
                  ).decode("utf-8")
              )
              self.log.info(
                  "Loaded fields from schema object: %s",
                  self.schema_fields,
              )
          else:
              self.schema_fields = None
  
      if self.external_table:
          self.log.info(
              "Creating a new BigQuery table for storing data..."
          )
          table_obj_api_repr = self._create_external_table()
  
          BigQueryTableLink.persist(
              context=context,
              task_instance=self,
              dataset_id=table_obj_api_repr["tableReference"][
                  "datasetId"
              ],
              project_id=table_obj_api_repr["tableReference"][
                  "projectId"
              ],
              table_id=table_obj_api_repr["tableReference"]["tableId"],
          )
          if self.max_id_key:
              max_id = self._find_max_value_in_column()
              return max_id
      else:
          self.log.info(
              "Using existing BigQuery table for storing data..."
          )
          self.configuration = self._use_existing_table()
  
          try:
              self.log.info("Executing: %s", self.configuration)
              job: BigQueryJob | UnknownJob = self._submit_job(
                  self.hook, job_id
              )
          except Conflict:
              # If the job already exists retrieve it
              job = self.hook.get_job(
                  project_id=self.project_id or self.hook.project_id,
                  location=self.location,
                  job_id=job_id,
              )
              if job.state in self.reattach_states:
                  # We are reattaching to a job
                  job._begin()
                  self._handle_job_error(job)
              else:
                  # Same job configuration so we need force_rerun
                  raise AirflowException(
                      f"Job with id: {job_id} already exists and is in {job.state} state. If you "
                      f"want to force rerun it consider setting `force_rerun=True`."
                      f"Or, if you want to reattach in this scenario add {job.state} to `reattach_states`"
                  )
  
          job_types = {
              LoadJob._JOB_TYPE: ["sourceTable", "destinationTable"],
              CopyJob._JOB_TYPE: ["sourceTable", "destinationTable"],
              ExtractJob._JOB_TYPE: ["sourceTable"],
              QueryJob._JOB_TYPE: ["destinationTable"],
          }
  
          if self.hook.project_id:
              for job_type, tables_prop in job_types.items():
                  job_configuration = job.to_api_repr()["configuration"]
                  if job_type in job_configuration:
                      for table_prop in tables_prop:
                          if table_prop in job_configuration[job_type]:
                              table = job_configuration[job_type][
                                  table_prop
                              ]
                              persist_kwargs = {
                                  "context": context,
                                  "task_instance": self,
                                  "table_id": table,
                              }
                              if not isinstance(table, str):
                                  persist_kwargs["table_id"] = table[
                                      "tableId"
                                  ]
                                  persist_kwargs["dataset_id"] = table[
                                      "datasetId"
                                  ]
                                  persist_kwargs["project_id"] = table[
                                      "projectId"
                                  ]
                              BigQueryTableLink.persist(
                                  **persist_kwargs
                              )
  
          self.job_id = job.job_id
          context["ti"].xcom_push(key="job_id", value=self.job_id)
          if self.deferrable:
              self.defer(
                  timeout=self.execution_timeout,
                  trigger=BigQueryInsertJobTrigger(
                      conn_id=self.gcp_conn_id,
                      job_id=self.job_id,
                      project_id=self.project_id
                      or self.hook.project_id,
                  ),
                  method_name="execute_complete",
              )
          else:
              job.result(
                  timeout=self.result_timeout, retry=self.result_retry
              )
              self._handle_job_error(job)
              if self.max_id_key:
                  return self._find_max_value_in_column()
  
imports: from __future__ import annotations
  import json
  from typing import TYPE_CHECKING, Any, Sequence
  from google.api_core.exceptions import BadRequest, Conflict
  from google.cloud.bigquery import DEFAULT_RETRY, CopyJob, ExternalConfig, ExtractJob, LoadJob, QueryJob, SchemaField, UnknownJob
  from google.cloud.bigquery.table import EncryptionConfiguration, Table, TableReference
  from airflow.configuration import conf
  from airflow.exceptions import AirflowException
  from airflow.models import BaseOperator
  from airflow.providers.google.cloud.hooks.bigquery import BigQueryHook, BigQueryJob
  from airflow.providers.google.cloud.hooks.gcs import GCSHook
  from airflow.providers.google.cloud.links.bigquery import BigQueryTableLink
  from airflow.providers.google.cloud.triggers.bigquery import BigQueryInsertJobTrigger
  from airflow.utils.helpers import merge_dicts
label_desc: Use image node_id 4 for calling the GCSToBigQueryOperator obj's underlying member method code with example usage: obj.execute(context) and returns: max_id, self