class_docstr: Main execution class for training backends.
  
  This class holds a worker group and is responsible for executing the
  training function on the workers, and collecting intermediate results
  from ``session.report()``.
  
  Args:
      backend_config: The configurations for this
          specific backend.
      num_workers: Number of workers to use for training.
      num_cpus_per_worker: Number of CPUs to use per worker.
      num_gpus_per_worker: Number of GPUs to use per worker.
      additional_resources_per_worker (Optional[Dict[str, float]]):
          Dictionary specifying the extra resources that will be
          requested for each worker in addition to ``num_cpus_per_worker``
          and ``num_gpus_per_worker``.
      max_retries: Number of retries when Ray actors fail.
          Defaults to 3. Set to -1 for unlimited retries.
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docstr: Disable workers from enqueuing results from ``session.report()``.
  
  Note: Already reported results may still be enqueued at this point,
        and should be handled appropriately.
docstr_tok: ["Disable","workers","from","enqueuing","results","from","``","session.report","(",")","``",".","Note",":","Already","reported","results","may","still","be","enqueued","at","this","point",",","and","should","be","handled","appropriately","."]
code: def pause_reporting(self):
      """Disable workers from enqueuing results from ``session.report()``.
  
      Note: Already reported results may still be enqueued at this point,
            and should be handled appropriately.
      """
  
      def pause_session_reporting():
          session = _get_session("pause_reporting")
          return session.pause_reporting()
  
      futures = self.worker_group.execute_async(pause_session_reporting)
      self.get_with_failure_handling(futures)
  
imports: import logging
  import os
  from collections import defaultdict
  from dataclasses import dataclass
  from typing import Any, Callable, Dict, List, Optional, Tuple, Type, TypeVar
  import ray
  import ray._private.ray_constants
  from ray._private.ray_constants import env_integer
  from ray.data import Dataset
  from ray.exceptions import RayActorError
  from ray.train import Checkpoint, DataConfig
  from ray.train._internal.session import TrialInfo, _TrainingResult, get_session, init_session, shutdown_session
  from ray.train._internal.storage import StorageContext
  from ray.train._internal.utils import check_for_failure
  from ray.train._internal.worker_group import WorkerGroup
  from ray.train.backend import BackendConfig
  from ray.train.constants import ENABLE_DETAILED_AUTOFILLED_METRICS_ENV, ENABLE_SHARE_CUDA_VISIBLE_DEVICES_ENV, ENABLE_SHARE_NEURON_CORES_ACCELERATOR_ENV, TRAIN_ENABLE_WORKER_SPREAD_ENV, TRAIN_PLACEMENT_GROUP_TIMEOUT_S_ENV
  from ray.util.placement_group import get_current_placement_group, remove_placement_group
label_desc: Use image node_id 10 for calling the BackendExecutor obj's underlying member method code with example usage: obj.pause_reporting() and returns: session