code: def slice_tensors(
      data, tensor_slice, process_index=None, num_processes=None
  ):
      """
      Recursively takes a slice in a nested list/tuple/dictionary of tensors.
  
      Args:
          data (nested list/tuple/dictionary of `torch.Tensor`):
              The data to slice.
          tensor_slice (`slice`):
              The slice to take.
  
      Returns:
          The same data structure as `data` with all the tensors slices.
      """
  
      def _slice_tensor(tensor, tensor_slice):
          return tensor[tensor_slice]
  
      return recursively_apply(_slice_tensor, data, tensor_slice)
  
imports: import pickle
  import warnings
  from functools import update_wrapper, wraps
  from typing import Any, Mapping
  import torch
  from ..state import PartialState
  from .constants import TORCH_DISTRIBUTED_OPERATION_TYPES
  from .dataclasses import DistributedType, TensorInformation
  from .imports import is_npu_available, is_torch_distributed_available, is_torch_version, is_tpu_available
label_desc: Use image node_id 24 for calling a global function with example usage: slice_tensors(data, tensor_slice, process_index, num_processes) and returns: recursively_apply, tensor