text: //*****************************************************************************
  // Copyright 2017-2020 Intel Corporation
  //
  // Licensed under the Apache License, Version 2.0 (the "License");
  // you may not use this file except in compliance with the License.
  // You may obtain a copy of the License at
  //
  //     http://www.apache.org/licenses/LICENSE-2.0
  //
  // Unless required by applicable law or agreed to in writing, software
  // distributed under the License is distributed on an "AS IS" BASIS,
  // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  // See the License for the specific language governing permissions and
  // limitations under the License.
  //*****************************************************************************
  
  #include "gtest/gtest.h"
  #include "ngraph/ngraph.hpp"
  #include "ngraph/runtime/tensor.hpp"
  #include "runtime/backend.hpp"
  #include "util/all_close_f.hpp"
  #include "util/ndarray.hpp"
  #include "util/random.hpp"
  #include "util/test_control.hpp"
  #include "util/test_tools.hpp"
  
  using namespace std;
  using namespace ngraph;
  
  static string s_manifest = "${MANIFEST}";
  
  // This tests a backend's implementation of the two parameter version of create_tensor
  NGRAPH_TEST(${BACKEND_NAME}, create_tensor_1)
  {
      Shape shape{2, 2};
      auto A = make_shared<op::Parameter>(element::Type_t::f32, shape);
      auto B = make_shared<op::Parameter>(element::Type_t::f32, shape);
      auto f = make_shared<Function>(make_shared<op::v1::Add>(A, B), ParameterVector{A, B});
  
      auto backend = runtime::Backend::create("${BACKEND_NAME}");
  
      // Create some tensors for input/output
      vector<float> av = {1, 2, 3, 4};
      vector<float> bv = {5, 6, 7, 8};
      shared_ptr<runtime::Tensor> a = backend->create_tensor(element::Type_t::f32, shape);
      shared_ptr<runtime::Tensor> b = backend->create_tensor(element::Type_t::f32, shape);
      copy_data(a, av);
      copy_data(b, bv);
  
      shared_ptr<runtime::Tensor> result = backend->create_tensor(element::Type_t::f32, shape);
  
      auto handle = backend->compile(f);
      handle->call_with_validate({result}, {a, b});
      vector<float> expected = {6, 8, 10, 12};
      EXPECT_TRUE(test::all_close_f(read_vector<float>(result), expected, MIN_FLOAT_TOLERANCE_BITS));
  }
  
  NGRAPH_TEST(${BACKEND_NAME}, get_parameters_and_results)
  {
      Shape shape{2, 2};
      auto A = make_shared<op::Parameter>(element::Type_t::f32, shape);
      auto B = make_shared<op::Parameter>(element::Type_t::f32, shape);
      auto C = make_shared<op::Parameter>(element::Type_t::f32, shape);
      auto arg = make_shared<op::v1::Multiply>(make_shared<op::v1::Add>(A, B), C);
      auto f = make_shared<Function>(arg, ParameterVector{A, B, C});
  
      auto backend = runtime::Backend::create("${BACKEND_NAME}");
  
      // Create some tensors for input/output
      shared_ptr<runtime::Tensor> a = backend->create_tensor(element::Type_t::f32, shape);
      shared_ptr<runtime::Tensor> b = backend->create_tensor(element::Type_t::f32, shape);
      shared_ptr<runtime::Tensor> c = backend->create_tensor(element::Type_t::f32, shape);
      shared_ptr<runtime::Tensor> result = backend->create_tensor(element::Type_t::f32, shape);
  
      copy_data(a, test::NDArray<float, 2>({{1, 2}, {3, 4}}).get_vector());
      copy_data(b, test::NDArray<float, 2>({{5, 6}, {7, 8}}).get_vector());
      copy_data(c, test::NDArray<float, 2>({{9, 10}, {11, 12}}).get_vector());
  
      auto handle = backend->compile(f);
      auto parameters = handle->get_parameters();
      auto results = handle->get_results();
      ASSERT_EQ(parameters.size(), 3);
      ASSERT_EQ(results.size(), 1);
  
      // This part can't be enabled until we force backends to make a copy of the source graph
      // auto func_parameters = f->get_parameters();
      // auto func_results = f->get_results();
      // for (size_t i = 0; i < 3; ++i)
      // {
      //     EXPECT_NE(parameters[i], func_parameters[i]);
      // }
      // for (size_t i = 0; i < 1; ++i)
      // {
      //     EXPECT_NE(results[i], func_results[i]);
      // }
  }
  