code: def get_allocator_backend() -> str:
      r"""Return a string describing the active allocator backend as set by
      ``PYTORCH_CUDA_ALLOC_CONF``. Currently available backends are
      ``native`` (PyTorch's native caching allocator) and `cudaMallocAsync``
      (CUDA's built-in asynchronous allocator).
  
      .. note::
          See :ref:`cuda-memory-management` for details on choosing the allocator backend.
      """
      return torch._C._cuda_getAllocatorBackend()
  
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 31 for calling a global function with example usage: get_allocator_backend() and returns: torch