old_contents: import chainerx
  from chainerx import _pybind_cuda
  
  try:
      import cupy
      _cupy_available = True
  except Exception:
      _cupy_available = False
  
  
  _chainerx_allocator = None
  
  
  def cupy_share_allocator(owner=chainerx._global_context):
      # Replace CuPy's allocator with ChainerX's if ChainerX is available with
      # the CUDA backend. This is needed in order to share the GPU memory
      # without having both modules using separate memory pools.
  
      # TODO(imanishi): Make sure this allocator works when the global
      # default context is changed by the user. It currently will not
      # since the allocator is only configured here once.
      try:
          owner.get_backend('cuda')
      except chainerx.BackendError:
          raise RuntimeError(
              'Cannot share allocator with CuPy without the CUDA backend.')
  
      if not _cupy_available:
          raise RuntimeError(
              'Cannot share allocator with CuPy since CuPy is not available.')
  
      param = _pybind_cuda.get_backend_ptr()
      malloc_func, free_func = _pybind_cuda.get_backend_malloc_free_ptrs()
  
      global _chainerx_allocator
      _chainerx_allocator = cupy.cuda.memory.CFunctionAllocator(
          param, malloc_func, free_func, owner)
  
      cupy.cuda.set_allocator(_chainerx_allocator.malloc)
  
new_contents: import chainerx
  
  try:
      import cupy
      _cupy_available = True
  except Exception:
      _cupy_available = False
  
  
  _chainerx_allocator = None
  
  
  def cupy_share_allocator(owner=chainerx._global_context):
      # Replace CuPy's allocator with ChainerX's if ChainerX is available with
      # the CUDA backend. This is needed in order to share the GPU memory
      # without having both modules using separate memory pools.
  
      # TODO(imanishi): Make sure this allocator works when the global
      # default context is changed by the user. It currently will not
      # since the allocator is only configured here once.
      try:
          owner.get_backend('cuda')
      except chainerx.BackendError:
          raise RuntimeError(
              'Cannot share allocator with CuPy without the CUDA backend.')
  
      if not _cupy_available:
          raise RuntimeError(
              'Cannot share allocator with CuPy since CuPy is not available.')
  
      param = chainerx._pybind_cuda.get_backend_ptr()
      malloc_func, free_func = (
          chainerx._pybind_cuda.get_backend_malloc_free_ptrs())
  
      global _chainerx_allocator
      _chainerx_allocator = cupy.cuda.memory.CFunctionAllocator(
          param, malloc_func, free_func, owner)
  
      cupy.cuda.set_allocator(_chainerx_allocator.malloc)
  
subject: Fix import error when CUDA is not available