code: def test_reduce_front_sum_empty_batch(
      self, num_reduce_dim, seed, gc, dc
  ):
      np.random.seed(seed)
      X = np.random.rand(0, 4, 3, 5).astype(np.float32)
  
      def ref_sum(X):
          return [np.sum(X, axis=(tuple(range(num_reduce_dim))))]
  
      self.reduce_op_test(
          "ReduceFrontSum", ref_sum, [X], ["input"], num_reduce_dim, gc
      )
      self.grad_variant_input_test(
          "ReduceFrontSumGradient", X, ref_sum, num_reduce_dim
      )
  
      # test the second iteration
      not_empty_X = np.random.rand(2, 4, 3, 5).astype(np.float32)
      net = core.Net("test")
      with core.DeviceScope(gc):
          net.ReduceFrontSum(
              ["X"], ["output"], num_reduce_dim=num_reduce_dim
          )
          workspace.CreateNet(net)
  
          workspace.FeedBlob("X", not_empty_X)
          workspace.RunNet(workspace.GetNetName(net))
          output = workspace.FetchBlob("output")
          np.testing.assert_allclose(
              output, ref_sum(not_empty_X)[0], atol=1e-3
          )
  
          workspace.FeedBlob("X", X)
          workspace.RunNet(workspace.GetNetName(net))
          output = workspace.FetchBlob("output")
          np.testing.assert_allclose(output, ref_sum(X)[0], atol=1e-3)
  
imports: from caffe2.python import core, workspace
  from hypothesis import given, settings
  import caffe2.python.hypothesis_test_util
  import caffe2.python.serialized_test.serialized_test_util
  import hypothesis.strategies
  import numpy
  import itertools
label_desc: Use image node_id 5 for calling the TestReduceFrontReductions obj's underlying member method code with example usage: obj.test_reduce_front_sum_empty_batch(num_reduce_dim, seed, gc, dc) and returns: list