code: def run_map_helper(self, thread_id):
      start_idx, end_idx = (
          self.thread_splits[thread_id][0],
          self.thread_splits[thread_id][1],
      )
      logger.info(
          f"worker {self.worker_id} thread {thread_id}: start working "
          f"on data subset {start_idx} to {end_idx}"
      )
      thread_dataset = Subset(
          self.dataset, list(range(start_idx, end_idx))
      )
      sampler = BatchSampler(
          SequentialSampler(thread_dataset),
          batch_size=self.batch_size,
          drop_last=False,
      )
      if self.collate_fn is None:
          iterator = iter(
              DataLoader(
                  thread_dataset,
                  batch_sampler=sampler,
                  num_workers=0,
                  pin_memory=False,
              )
          )
      else:
          iterator = iter(
              DataLoader(
                  thread_dataset,
                  batch_sampler=sampler,
                  num_workers=0,
                  collate_fn=self.collate_fn,
                  pin_memory=False,
              )
          )
      if self.custom_map_init is None:
          metric_results = self.init_metric_results(
              thread_id,
              self.metric_names,
              self.metric_types,
              self.metric_dtypes,
              self.save_path,
              self.worker_id,
          )
      else:
          metric_results = self.custom_map_init(
              thread_id,
              self.metric_names,
              self.metric_types,
              self.metric_dtypes,
              self.save_path,
              self.worker_id,
          )
      total_sample = len(thread_dataset)
      processed_sample = 0
      start = time.time()
      while True:
          try:
              data = next(iterator)
              if self.custom_map_update is None:
                  self.update_metric_results(
                      data,
                      self.metric_types,
                      self.metric_functions,
                      metric_results,
                  )
              else:
                  self.custom_map_update(
                      data,
                      self.metric_types,
                      self.metric_functions,
                      metric_results,
                  )
              processed_sample += self.batch_size
              duration = (time.time() - start) / 3600.0
              remain_duration = (
                  duration * total_sample / processed_sample - duration
              )
              logger.info(
                  f"worker {self.worker_id} thread {thread_id}: {processed_sample} "
                  f"out of {total_sample} processed in {duration:.2f} hr, "
                  f"estimated to finish in {remain_duration:.2f} hr"
              )
          except StopIteration:
              logger.info(
                  f"worker {self.worker_id} thread {thread_id}: reach end of file"
              )
              break
      if self.custom_map_finalize is None:
          self.finalize_metric_results(
              self.metric_types, self.metric_dtypes, metric_results
          )
      else:
          self.custom_map_finalize(
              self.metric_types, self.metric_dtypes, metric_results
          )
      logger.info(
          f"worker {self.worker_id} thread {thread_id}: finished"
      )
  
imports: import os
  from collections import defaultdict
  import csv
  import time
  from multiprocessing import Process, Manager
  import numpy
  import torch
  from torch.utils.data import BatchSampler, SequentialSampler, DataLoader, Subset
  from deepspeed.utils import logger
  from .indexed_dataset import MMapIndexedDataset
  from .utils import split_dataset, split_index, create_mmap_dataset_builder, close_mmap_dataset_builder, find_fit_int_dtype
label_desc: Use image node_id 5 for calling the DataAnalyzer obj's underlying member method code with example usage: obj.run_map_helper(thread_id) without return types