code: def test_cpu2gpu_gpu2cpu_sparse_gradients(self):
      model = model_helper.ModelHelper(name="copy_test")
      v = model.param_init_net.UniformFill([], ["v"], shape=[16, 4])
      indices = model.param_init_net.UniformFill(
          [], ["v"], shape=[16, 4]
      )
      cpu_opt = core.DeviceOption(caffe2_pb2.CPU, 0)
      gpu_opt = core.DeviceOption(workspace.GpuDeviceType, 0)
  
      with core.DeviceScope(gpu_opt):
          vcpu = model.CopyGPUToCPU(v, "vcpu")
  
      with core.DeviceScope(cpu_opt):
          g = model.Gather([vcpu, indices], "g")
  
      with core.DeviceScope(gpu_opt):
          ggpu = model.CopyCPUToGPU(g, "ggpu")
          f = brew.fc(model, ggpu, "out", dim_in=4, dim_out=6)
          (softmax, loss) = model.SoftmaxWithLoss(
              [f, "label"],
              ["softmax", "loss"],
          )
      gradient_map = model.AddGradientOperators([loss])
      self.assertTrue("v" in gradient_map)
      self.assertTrue(isinstance(gradient_map["v"], core.GradientSlice))
  
file_path: pytorch/caffe2/python/operator_test/copy_ops_test.py
imports: import numpy
  import unittest
  from caffe2.proto import caffe2_pb2
  from caffe2.python import workspace, core, model_helper, brew, test_util
label_desc: Use image node_id 6 for calling the CopyOpsTest obj's underlying member method code with example usage: obj.test_cpu2gpu_gpu2cpu_sparse_gradients() without return types