code: def cupy_share_allocator():
      # 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.
  
      if not _available:
          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."
          )
  
      c_allocator = _pybind_cuda.get_c_allocator()
  
      chainerx_allocator = cupy.cuda.memory.CFunctionAllocator(
          *c_allocator, chainerx._global_context
      )
  
      cupy.cuda.set_allocator(chainerx_allocator.malloc)
  
label_desc: Use image node_id 1 for calling a global function with example usage: cupy_share_allocator() without return types