TFE_NewOp(context.get(), "AssertAndCollective", status.get()),
      TFE_DeleteOp);
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());
  std::unique_ptr<ParallelTensor> reduced_values =
      parallel_device.ScalarsFromSequence<float>({1.0, 2.0}, context.get(),
                                                 status.get());
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());

  TF_SetAttrString(reduce_desc, "final_op", final_op.c_str(),
                   final_op.length());
  TF_SetAttrIntList(reduce_desc, "subdiv_offsets", nullptr, 0);
  TF_AddInput(reduce_desc, x);
  TF_Operation* reduce_op = TF_FinishOperation(reduce_desc, status);
  if (TF_GetCode(status) != TF_OK) return;
  TF_Operation* operations[]{placeholder_op, reduce_op};
  TF_Output y{reduce_op, 0};
  const char* output_name = "y";

        // Non-parallel tensors from _EagerConst/tf.constant are implicitly
        // broadcast, i.e. set as the input to each parallel operation. This
        // allows code like "tf.constant(1.)" or "tf.reduce_sum(..., axis=1)"
        // (where the value starts on the host), without allowing other implicit
        // copies/broadcasts. Other implicit copies may be supported eventually,