code: def _shuffle_tensor_slice(
      *,
      tensor: torch.Tensor,
      start: int = 0,
      step: int = 1,
      generator: torch.Generator,
  ) -> np.ndarray:
      stop = len(tensor)
      count = stop // step
      drop_count = stop - step * count
      if drop_count:
          warnings.warn(f"# of dropped samples: {drop_count}")
  
      dtype = _get_numpy_dtype(stop)
      result = np.empty(count, dtype=dtype)
  
      for i in range(count):
          j = (
              torch.randint(
                  0, i + 1, size=(1,), generator=generator
              ).item()
              if i > 0
              else 0
          )
  
          result[i] = result[j]
          result[j] = tensor[start + i * step].item()
  
      return result
  
imports: import itertools
  from typing import Any, Optional
  import warnings
  import numpy
  import torch
  from torch.utils.data.sampler import Sampler
  import dinov2.distributed
label_desc: Use image node_id 4 for calling a global function with example usage: _shuffle_tensor_slice() and returns: result