code: def test_gather_for_metrics_with_non_tensor_objects_iterable_dataset():
      class DummyIterableDataset(IterableDataset):
          def __init__(self, data):
              self.data = data
  
          def __len__(self):
              return len(self.data)
  
          def __iter__(self):
              for element in self.data:
                  yield element
  
      iterable_dataset = DummyIterableDataset([n for n in range(30)])
      dataloader = DataLoader(iterable_dataset, batch_size=4)
      accelerator = Accelerator()
      prepared_dataloader = accelerator.prepare(dataloader)
  
      if accelerator.is_main_process:
          logger = logging.root.manager.loggerDict[
              "accelerate.accelerator"
          ]
          list_handler = ListHandler()
          logger.addHandler(list_handler)
  
      batches_for_metrics = []
      for batch in prepared_dataloader:
          batches_for_metrics.append(
              accelerator.gather_for_metrics(batch)
          )
  
      assert torch.cat(batches_for_metrics).size(0) == 30
  
      if accelerator.is_main_process:
          assert len(list_handler.logs) == 0
          logger.removeHandler(list_handler)
  
imports: import logging
  import math
  import os
  from copy import deepcopy
  import datasets
  import evaluate
  import torch
  import transformers
  from datasets import load_dataset
  from torch.utils.data import DataLoader, IterableDataset
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  from accelerate import Accelerator
  from accelerate.data_loader import DataLoaderDispatcher
  from accelerate.test_utils import RegressionDataset, RegressionModel, torch_device
  from accelerate.utils import set_seed
label_desc: Use image node_id 7 for calling a global function with example usage: test_gather_for_metrics_with_non_tensor_objects_iterable_dataset() and returns: len