code: def empty_cache() -> None:
      r"""Release all unoccupied cached memory currently held by the caching
      allocator so that those can be used in other GPU application and visible in
      `nvidia-smi`.
  
      .. note::
          :func:`~torch.cuda.empty_cache` doesn't increase the amount of GPU
          memory available for PyTorch. However, it may help reduce fragmentation
          of GPU memory in certain cases. See :ref:`cuda-memory-management` for
          more details about GPU memory management.
      """
      if is_initialized():
          torch._C._cuda_emptyCache()
  
imports: import collections
  import contextlib
  import ctypes
  import pickle
  import sys
  import warnings
  from inspect import signature
  from typing import Any, Dict, Optional, Tuple, Union
  import torch
  from torch import _C
  from torch.types import Device
  from .None import _get_device_index, _get_nvml_device_index, _lazy_init, is_initialized
  from ._memory_viz import memory, segments
  from ._utils import _dummy_type
label_desc: Use image node_id 6 for calling a global function with example usage: empty_cache() without return types