code: def test_gather_for_metrics_drop_last():
      accelerator = Accelerator()
      per_device_batch_size = 5
      num_items = (10 * accelerator.num_processes) + 1
      dataloader = DataLoader(
          range(num_items),
          batch_size=per_device_batch_size,
          drop_last=True,
      )
      dataloader = accelerator.prepare(dataloader)
  
      iterator = iter(dataloader)
      next(
          iterator
      )  # Skip first batch tensor([0, 1, 2, 3, 4], device='cuda:0')
      batch = next(iterator)
      gathered_items = accelerator.gather_for_metrics(batch)
  
      # Should return a full set of complete batches from each GPU
      num_expected_items = (
          per_device_batch_size * accelerator.num_processes
      )
      assert gathered_items.size(0) == (
          num_expected_items
      ), f"Expected number of items: {num_expected_items}, Actual: {gathered_items.size(0)}"
  
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 9 for calling a global function with example usage: test_gather_for_metrics_drop_last() without return types