code: def test_convolution_sync(
      self, net_type, num_workers, engine, gc, dc
  ):
      m = ModelHelper(name="test_model")
      n = 1
      d = 2
      depth = 3
      iters = 5
      h = 5
      w = 5
      workspace.ResetWorkspace()
  
      use_cudnn = engine == "CUDNN"
  
      np.random.seed(1701)
      # Build a binary tree of conv layers, summing at each node.
      for i in reversed(range(depth)):
          for j in range(2**i):
              bottom_1 = "{}_{}".format(i + 1, 2 * j)
              bottom_2 = "{}_{}".format(i + 1, 2 * j + 1)
              mid_1 = "{}_{}_m".format(i + 1, 2 * j)
              mid_2 = "{}_{}_m".format(i + 1, 2 * j + 1)
              top = "{}_{}".format(i, j)
              w1, b1, w2, b2 = np.random.randn(4).tolist()
              brew.conv(
                  m,
                  bottom_1,
                  mid_1,
                  dim_in=d,
                  dim_out=d,
                  kernel=3,
                  weight_init=("ConstantFill", {"value": w1}),
                  bias_init=("ConstantFill", {"value": b1}),
                  cudnn_state=np.random.randint(0, 3),
                  stride=1,
                  pad=1,
                  deterministic=1,
                  use_cudnn=use_cudnn,
                  engine=engine,
              )
              brew.conv(
                  m,
                  bottom_2,
                  mid_2,
                  dim_in=d,
                  dim_out=d,
                  kernel=3,
                  stride=1,
                  pad=1,
                  weight_init=("ConstantFill", {"value": w2}),
                  bias_init=("ConstantFill", {"value": b2}),
                  deterministic=1,
                  cudnn_state=np.random.randint(0, 3),
                  use_cudnn=use_cudnn,
                  engine=engine,
              )
              m.net.Sum([mid_1, mid_2], top)
  
      m.net.Flatten(["0_0"], ["0_0_flat"])
      m.net.SquaredL2Distance(["0_0_flat", "label"], "xent")
      m.net.AveragedLoss("xent", "loss")
      input_to_grad = m.AddGradientOperators(["loss"])
      m.Proto().device_option.CopyFrom(gc)
      m.param_init_net.Proto().device_option.CopyFrom(gc)
      m.Proto().type = net_type
      m.Proto().num_workers = num_workers
      self.ws.run(m.param_init_net)
  
      def run():
          import numpy as np
  
          np.random.seed(1701)
          input_blobs = [
              "{}_{}".format(depth, j) for j in range(2**depth)
          ]
          for input_blob in input_blobs:
              self.ws.create_blob(input_blob).feed(
                  np.random.randn(n, d, h, w).astype(np.float32),
                  device_option=gc,
              )
              self.ws.create_blob("label").feed(
                  np.random.randn(n, d * h * w).astype(np.float32),
                  device_option=gc,
              )
          self.ws.run(m.net)
          gradients = [
              self.ws.blobs[str(input_to_grad[input_blob])].fetch()
              for input_blob in input_blobs
          ]
          return gradients
  
      outputs = [run() for _ in range(iters)]
      for output in outputs[1:]:
          np.testing.assert_array_equal(outputs[0], output)
          np.testing.assert_allclose(
              np.sum(np.square(output)), 1763719461732352.0, rtol=1e-5
          )
  
imports: import collections
  import functools
  import unittest
  import caffe2.python._import_c_extension
  import caffe2.python.hip_test_util
  import caffe2.python.hypothesis_test_util
  import caffe2.python.serialized_test.serialized_test_util
  import hypothesis.strategies
  import numpy
  from caffe2.proto import caffe2_pb2
  from caffe2.python import brew, core, utils, workspace
  from caffe2.python.model_helper import ModelHelper
  from hypothesis import assume, given, settings
label_desc: Use image node_id 9 for calling the TestConvolution obj's underlying member method code with example usage: obj.test_convolution_sync(net_type, num_workers, engine, gc, dc) and returns: gradients