code: def torch_batch_process(
      batch_processor_cls: Type[TorchBatchProcessor],
      dataset: data.Dataset,
      batch_size: Optional[int] = None,
      max_batches: Optional[int] = None,
      checkpoint_interval: int = 5,
      dataloader_kwargs: Optional[Dict[str, Any]] = None,
      distributed_context: Optional[core.DistributedContext] = None,
  ) -> None:
      """
      ```torch_batch_process``` shard and iterate through the provided dataset and process the dataset
      with user-defined logic in ```batch_processor_cls```.
  
      Arguments:
          batch_processor_cls: A user-defined class extending ```TorchBatchProcessor```
          dataset: A torch dataset class implementing __len__() and __getitem__()
          batch_size: The number of items to in each batch
          max_batches: The maximum number of batches to iterate over per worker
          checkpoint_interval: Interval to checkpoint progress (i.e. record number
              of batches processed)
          dataloader_kwargs: Kwargs to pass to PyTorch dataloader
          distributed_context: Distributed context to initialize core context
      """
      with _initialize_default_inference_context(
          distributed_context
      ) as core_context:
          """
          (1) Set up necessary variables to run batch processing
          """
  
          # Validate argument inputs
          if checkpoint_interval <= 0:
              raise ValueError(
                  "checkpoint_interval should be a positive integer"
              )
  
          if dataloader_kwargs is None:
              dataloader_kwargs = {}
          _validate_dataloader_kwargs(dataloader_kwargs, batch_size)
  
          if batch_size is None:
              if "batch_size" in dataloader_kwargs:
                  # remove batch_size from dataloader_kwargs
                  # and assign to batch_size
                  batch_size = dataloader_kwargs.pop("batch_size")
              else:
                  batch_size = DEFAULT_BATCH_SIZE
          if not isinstance(dataset, Sized):
              raise Exception("Dataset must implement __len__()")
  
          dataset_len = len(dataset)
  
          info = det.get_cluster_info()
  
          if info is None:
              raise Exception(
                  "torch_batch_process only runs on-cluster."
              )
  
          slots_per_node = len(info.slot_ids)
  
          num_nodes = len(info.container_addrs)
          total_worker = num_nodes
          if slots_per_node > 0:
              total_worker *= slots_per_node
          # Get global rank
          rank = core_context.distributed.get_rank()
          latest_checkpoint = info.latest_checkpoint
          skip = 0
  
          # Synchronize default output uuid
          if rank == 0:
              default_output_uuid = str(uuid.uuid4())
              core_context.distributed.broadcast(default_output_uuid)
          else:
              default_output_uuid = core_context.distributed.broadcast(
                  None
              )
  
          # Get previous trial state from checkpoint if available
          if latest_checkpoint is not None:
              logger.info("Checkpoint is not none")
              with core_context.checkpoint.restore_path(
                  latest_checkpoint
              ) as path:
                  metadata = _load_state(path)
                  skip = metadata["steps_completed"]
                  logger.info(f"Previous run completed {skip} steps")
                  default_output_uuid = metadata["default_output_uuid"]
  
          output_uuid_with_rank = default_output_uuid + f"/rank_{rank}"
  
          batch_processor_context = TorchBatchProcessorContext(
              core_context, output_uuid_with_rank
          )
  
          per_batch_processor = batch_processor_cls(
              context=batch_processor_context,
          )
  
          dataloader = pytorch.DataLoader(
              dataset=dataset,
              batch_size=batch_size,
              shuffle=False,
              **dataloader_kwargs,
          ).get_data_loader(
              repeat=False,
              skip=skip,
              num_replicas=total_worker,
              rank=rank,
          )
  
          # Create dummy searcher op to report progress to master
          dummy_searcher_op = None
          # Initialize dummy searcher for progress report
          if rank == 0:
              dummy_searcher_op = core.DummySearcherOperation(1, True)
  
          dataloader_iterator = iter(dataloader)
  
          # Enumerate over dataloader directly may cause some workers to iterate for 1 more time
          # than others when drop_last = False. If those workers synchronize on the last batch_idx,
          # they would hang forever as other workers never hit that last batch_idx.
          # To avoid the issue, we calculate and take the ceiling of the iteration count to ensure
          # all workers iterate for the same number of times.
          dist_dataset_batch_count = math.ceil(
              dataset_len / batch_size / total_worker
          )
          iterate_length = _validate_iterate_length(
              max_batches, dist_dataset_batch_count
          )
  
          last_checkpoint_idx = -1
          batch_idx = skip
          steps_completed = skip
  
          """
          (2) Run batch processing
          """
          for batch_idx in range(skip, iterate_length):
              X = next(dataloader_iterator, None)
              if X is not None:
                  per_batch_processor.process_batch(
                      batch=X, batch_idx=batch_idx
                  )
              steps_completed = batch_idx + 1
  
              # Checkpoint and check preemption
              if (batch_idx + 1) % checkpoint_interval == 0:
                  logger.info(
                      f"Completed steps:  {steps_completed} and checkpointing"
                  )
  
                  per_batch_processor.on_checkpoint_start()
                  if core_context._tensorboard_manager is not None:
                      core_context._tensorboard_manager.sync()
                  _synchronize_and_checkpoint(
                      core_context, steps_completed, default_output_uuid
                  )
                  last_checkpoint_idx = batch_idx
  
                  # Report progress can only be done accurately with synchronization
                  # when rank == 0, dummy_searcher_op will be initialized, but lint is complaining
                  # therefore, adding additional check here
                  if rank == 0 and dummy_searcher_op is not None:
                      _report_progress_to_master(
                          dummy_searcher_op,
                          batch_idx,
                          total_worker,
                          batch_size,
                          dataset_len,
                      )
  
                  # Check preemption
                  if core_context.preempt.should_preempt():
                      # Finish reducing metrics and report to not lose state before preempting
                      _reduce_metrics(
                          batch_processor_context,
                          core_context,
                          rank,
                          steps_completed,
                      )
                      return
  
          """
          (3) Finish up after batch processing
          """
          if batch_idx > last_checkpoint_idx:
              per_batch_processor.on_checkpoint_start()
              logger.info(
                  f"Completed steps:  {steps_completed} and checkpointing"
              )
              _synchronize_and_checkpoint(
                  core_context, iterate_length, default_output_uuid
              )
  
          _reduce_metrics(
              batch_processor_context,
              core_context,
              rank,
              steps_completed,
          )
          # Finish any tensorboard uploads remaining
          if core_context._tensorboard_manager is not None:
              core_context._tensorboard_manager.sync()
  
          per_batch_processor.on_finish()
  
          # If user has used default storage, print out the default storage path
          if rank == 0 and batch_processor_context._use_default_storage:
              default_storage_path = _get_storage_information(
                  info.trial._config["checkpoint_storage"],
                  default_output_uuid,
                  core_context,
              )
              logger.info(
                  f"Files stored with default paths are at: {default_storage_path}"
              )
  
          # Perform gather here to ensure we only report progress to master when all workers finish
          core_context.distributed.gather(None)
  
          # when rank == 0, dummy_searcher_op will be initialized, but lint is complaining
          # therefore, adding additional check here
          if rank == 0 and dummy_searcher_op is not None:
              # Report to master the run has completed
              # Metrics reported does not matter
              dummy_searcher_op.report_completed(1)
  
imports: import abc
  import json
  import logging
  import math
  import os
  import pathlib
  import uuid
  import warnings
  from typing import TYPE_CHECKING, Any, ContextManager, Dict, Optional, Set, Sized, Type
  import torch
  import torch.distributed
  from torch import nn
  from torch.utils import data
  import determined
  from determined import common, core, pytorch
label_desc: Use image node_id 11 for calling a global function with example usage: torch_batch_process(batch_processor_cls, dataset, batch_size, max_batches, checkpoint_interval, dataloader_kwargs, distributed_context) without return types