code: def custom_sampler_check():
      state = AcceleratorState()
  
      class CustomDataset(Dataset):
          def __init__(self, data):
              self.data = data
  
          def __len__(self):
              return len(self.data)
  
          def __getitem__(self, index):
              return self.data[index]
  
      class CustomBatchSampler:
          def __init__(
              self,
              dataset_length: int,
              batch_size: int,
              shuffle: bool = True,
          ):
              self.batch_size = batch_size
              self.data_index = np.arange(dataset_length)
              self.shuffle = shuffle
  
          def __iter__(self):
              num_batches = len(self)
              if self.shuffle:
                  index = np.random.permutation(self.data_index)
              else:
                  index = self.data_index
              output = np.array_split(index, num_batches)
              yield from output
  
          def __len__(self):
              return math.ceil(len(self.data_index) / self.batch_size)
  
      dataset = CustomDataset(range(32 * state.num_processes))
      sampler = CustomBatchSampler(len(dataset), batch_size=8)
      dl = DataLoader(dataset, batch_sampler=sampler)
      dl = prepare_data_loader(
          dl, state.device, state.num_processes, state.process_index
      )
      # We need just ensure that `dl.batch_sampler` (or `dl.batch_sampler.batch_sampler` is indeed the old batch sampler
      if hasattr(dl.batch_sampler, "batch_sampler"):
          assert isinstance(
              dl.batch_sampler.batch_sampler, CustomBatchSampler
          ), "Custom sampler was changed after calling `prepare_data_loader`"
      else:
          assert isinstance(
              dl.batch_sampler, CustomBatchSampler
          ), "Custom sampler was changed after calling `prepare_data_loader`"
  
imports: import contextlib
  import io
  import math
  import time
  from copy import deepcopy
  from pathlib import Path
  import numpy
  import torch
  from torch.utils.data import DataLoader, Dataset
  from accelerate import Accelerator
  from accelerate.data_loader import SeedableRandomSampler, prepare_data_loader
  from accelerate.state import AcceleratorState
  from accelerate.test_utils import RegressionDataset, are_the_same_tensors
  from accelerate.utils import DistributedType, gather, is_bf16_available, is_ipex_available, is_npu_available, is_xpu_available, set_seed, synchronize_rng_states
label_desc: Use image node_id 10 for calling a global function with example usage: custom_sampler_check() and returns: len, self, math