code: def load_tensor_adapter(backend, version):
      """Tell DGL to load a tensoradapter library for given backend and version.
  
      Parameters
      ----------
      backend : str
          The backend (currently ``pytorch``, ``mxnet`` or ``tensorflow``).
      version : str
          The version number of the backend.
      """
      global tensor_adapter_loaded
      version = version.split("+")[0]
      if sys.platform.startswith("linux"):
          basename = "libtensoradapter_%s_%s.so" % (backend, version)
      elif sys.platform.startswith("darwin"):
          basename = "libtensoradapter_%s_%s.dylib" % (backend, version)
      elif sys.platform.startswith("win"):
          basename = "tensoradapter_%s_%s.dll" % (backend, version)
      else:
          raise NotImplementedError(
              "Unsupported system: %s" % sys.platform
          )
      path = os.path.join(_DIR_NAME, "tensoradapter", backend, basename)
      tensor_adapter_loaded = (
          _LIB.DGLLoadTensorAdapter(path.encode("utf-8")) == 0
      )
      if not tensor_adapter_loaded:
          logger = logging.getLogger("dgl-core")
          logger.debug(
              "Memory optimization with PyTorch is not enabled."
          )
  
file_path: dgl/python/dgl/_ffi/base.py
imports: from __future__ import absolute_import
  import ctypes
  import logging
  import os
  import sys
  import numpy
  from .None import libinfo
label_desc: Use image node_id 6 for calling a global function with example usage: load_tensor_adapter(backend, version) without return types