class_docstr: Process a Python file containing Airflow DAGs.
  
  This includes:
  
  1. Execute the file and look for DAG objects in the namespace.
  2. Execute any Callbacks if passed to DagFileProcessor.process_file
  3. Serialize the DAGs and save it to DB (or update existing record in the DB).
  4. Pickle the DAG and save it to the DB (if necessary).
  5. Record any errors importing the file into ORM
  
  Returns a tuple of 'number of dags found' and 'the count of import errors'
  
  :param dag_ids: If specified, only look at these DAG ID's
  :param log: Logger to save the processing process
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docstr: Process a Python file containing Airflow DAGs.
  
  This includes:
  
  1. Execute the file and look for DAG objects in the namespace.
  2. Execute any Callbacks if passed to this method.
  3. Serialize the DAGs and save it to DB (or update existing record in the DB).
  4. Pickle the DAG and save it to the DB (if necessary).
  5. Mark any DAGs which are no longer present as inactive
  6. Record any errors importing the file into ORM
  
  :param file_path: the path to the Python file that should be executed
  :param callback_requests: failure callback to execute
  :param pickle_dags: whether serialize the DAGs found in the file and
      save them to the db
  :param session: Sqlalchemy ORM Session
  :return: number of dags found, count of import errors
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code: def process_file(
      self,
      file_path: str,
      callback_requests: list[CallbackRequest],
      pickle_dags: bool = False,
      session: Session = NEW_SESSION,
  ) -> tuple[int, int]:
      """
      Process a Python file containing Airflow DAGs.
  
      This includes:
  
      1. Execute the file and look for DAG objects in the namespace.
      2. Execute any Callbacks if passed to this method.
      3. Serialize the DAGs and save it to DB (or update existing record in the DB).
      4. Pickle the DAG and save it to the DB (if necessary).
      5. Mark any DAGs which are no longer present as inactive
      6. Record any errors importing the file into ORM
  
      :param file_path: the path to the Python file that should be executed
      :param callback_requests: failure callback to execute
      :param pickle_dags: whether serialize the DAGs found in the file and
          save them to the db
      :param session: Sqlalchemy ORM Session
      :return: number of dags found, count of import errors
      """
      self.log.info("Processing file %s for tasks to queue", file_path)
  
      try:
          dagbag = DagFileProcessor._get_dagbag(file_path)
      except Exception:
          self.log.exception(
              "Failed at reloading the DAG file %s", file_path
          )
          Stats.incr(
              "dag_file_refresh_error",
              1,
              1,
              tags={"file_path": file_path},
          )
          return 0, 0
  
      if dagbag.dags:
          self.log.info(
              "DAG(s) %s retrieved from %s",
              ", ".join(map(repr, dagbag.dags)),
              file_path,
          )
      else:
          self.log.warning(
              "No viable dags retrieved from %s", file_path
          )
          DagFileProcessor.update_import_errors(
              file_last_changed=dagbag.file_last_changed,
              import_errors=dagbag.import_errors,
              processor_subdir=self._dag_directory,
              session=session,
          )
          if callback_requests:
              # If there were callback requests for this file but there was a
              # parse error we still need to progress the state of TIs,
              # otherwise they might be stuck in queued/running for ever!
              self.execute_callbacks_without_dag(
                  callback_requests, session
              )
          return 0, len(dagbag.import_errors)
  
      self.execute_callbacks(dagbag, callback_requests, session)
      session.commit()
  
      serialize_errors = DagFileProcessor.save_dag_to_db(
          dags=dagbag.dags,
          dag_directory=self._dag_directory,
          pickle_dags=pickle_dags,
      )
  
      dagbag.import_errors.update(dict(serialize_errors))
  
      # Record import errors into the ORM
      try:
          DagFileProcessor.update_import_errors(
              file_last_changed=dagbag.file_last_changed,
              import_errors=dagbag.import_errors,
              processor_subdir=self._dag_directory,
              session=session,
          )
      except Exception:
          self.log.exception("Error logging import errors!")
  
      # Record DAG warnings in the metadatabase.
      try:
          self.update_dag_warnings(session=session, dagbag=dagbag)
      except Exception:
          self.log.exception("Error logging DAG warnings.")
  
      return len(dagbag.dags), len(dagbag.import_errors)
  
imports: from __future__ import annotations
  import importlib
  import logging
  import os
  import signal
  import threading
  import time
  import zipfile
  from contextlib import redirect_stderr, redirect_stdout, suppress
  from datetime import timedelta
  from typing import TYPE_CHECKING, Iterable, Iterator
  from setproctitle import setproctitle
  from sqlalchemy import delete, func, or_, select
  from airflow import settings
  from airflow.api_internal.internal_api_call import internal_api_call
  from airflow.callbacks.callback_requests import DagCallbackRequest, SlaCallbackRequest, TaskCallbackRequest
  from airflow.configuration import conf
  from airflow.exceptions import AirflowException, TaskNotFound
  from airflow.models import SlaMiss, errors
  from airflow.models.dag import DAG, DagModel
  from airflow.models.dagbag import DagBag
  from airflow.models.dagrun import DagRun
  from airflow.models.dagwarning import DagWarning, DagWarningType
  from airflow.models.serialized_dag import SerializedDagModel
  from airflow.models.taskinstance import TaskInstance, TaskInstance
  from airflow.stats import Stats
  from airflow.utils import timezone
  from airflow.utils.email import get_email_address_list, send_email
  from airflow.utils.file import iter_airflow_imports, might_contain_dag
  from airflow.utils.log.logging_mixin import LoggingMixin, StreamLogWriter, set_context
  from airflow.utils.mixins import MultiprocessingStartMethodMixin
  from airflow.utils.session import NEW_SESSION, provide_session
  from airflow.utils.state import TaskInstanceState
label_desc: Use image node_id 11 for calling the DagFileProcessor obj's underlying member method code with example usage: obj.process_file(file_path, callback_requests, pickle_dags, session) and returns: len,  len, int,  len, int,  int