code: def _get_current_allocator() -> _CUDAAllocator:
      r"""Return the allocator being currently used.
  
      .. note::
          See :ref:`cuda-memory-management` for details on creating and using a custom allocator
      """
      return _CUDAAllocator(torch._C._cuda_getAllocator())
  
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 33 for calling a global function with example usage: _get_current_allocator() and returns: _CUDAAllocator