text: //*****************************************************************************
  // Copyright 2017-2019 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 <algorithm>
  #include <cinttypes>
  #include <cmath>
  #include <cstdlib>
  #include <random>
  #include <string>
  
  // clang-format off
  #ifdef ${BACKEND_NAME}_FLOAT_TOLERANCE_BITS
  #define DEFAULT_FLOAT_TOLERANCE_BITS ${BACKEND_NAME}_FLOAT_TOLERANCE_BITS
  #endif
  
  #ifdef ${BACKEND_NAME}_DOUBLE_TOLERANCE_BITS
  #define DEFAULT_DOUBLE_TOLERANCE_BITS ${BACKEND_NAME}_DOUBLE_TOLERANCE_BITS
  #endif
  // clang-format on
  
  #include "gtest/gtest.h"
  #include "ngraph/ngraph.hpp"
  #include "util/all_close.hpp"
  #include "util/all_close_f.hpp"
  #include "util/autodiff/numeric_compare.hpp"
  #include "util/ndarray.hpp"
  #include "util/test_control.hpp"
  #include "util/test_tools.hpp"
  
  using namespace std;
  using namespace ngraph;
  
  static string s_manifest = "${MANIFEST}";
  
  NGRAPH_TEST(${BACKEND_NAME}, gelu_f32)
  {
      Shape shape{8};
      auto A = make_shared<op::Parameter>(element::f32, shape);
      auto f = make_shared<Function>(make_shared<op::Gelu>(A), ParameterVector{A});
  
      auto backend = runtime::Backend::create("${BACKEND_NAME}");
  
      // Create some tensors for input/output
      auto a = backend->create_tensor(element::f32, shape);
      vector<float> input{-4.0f, -3.0f, -2.0f, -1.0f, 0.0f, 1.0f, 2.0f, 3.0f};
      copy_data(a, input);
      auto result = backend->create_tensor(element::f32, shape);
  
      std::transform(input.begin(), input.end(), input.begin(), [](float x) -> float {
          return 0.5f * x * (1.0f + erf(x / sqrt(2.0f)));
      });
  
      auto handle = backend->compile(f);
      handle->call_with_validate({result}, {a});
      EXPECT_TRUE(test::all_close_f(input, read_vector<float>(result)));
  }
  
  NGRAPH_TEST(${BACKEND_NAME}, gelu_f64)
  {
      Shape shape{8};
      auto A = make_shared<op::Parameter>(element::f64, shape);
      auto f = make_shared<Function>(make_shared<op::Gelu>(A), ParameterVector{A});
  
      auto backend = runtime::Backend::create("${BACKEND_NAME}");
  
      // Create some tensors for input/output
      auto a = backend->create_tensor(element::f64, shape);
      vector<double> input{-4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0};
      copy_data(a, input);
      auto result = backend->create_tensor(element::f64, shape);
  
      std::transform(input.begin(), input.end(), input.begin(), [](double x) -> double {
          return 0.5 * x * (1.0 + erf(x / sqrt(2.0)));
      });
  
      auto handle = backend->compile(f);
      handle->call_with_validate({result}, {a});
      EXPECT_TRUE(test::all_close_f(input, read_vector<double>(result)));
  }
  
  static double gelu_backprop_factor(double x)
  {
      auto pi = 4.0 * std::atan(1.0);
      return 0.5 * (1.0 + erf(x * sqrt(1.0 / 2.0))) + (x * exp(-x * x / 2.0)) / sqrt(2.0 * pi);
  }
  
  NGRAPH_TEST(${BACKEND_NAME}, gelu_backprop_factor_f32)
  {
      Shape shape{8};
      auto A = make_shared<op::Parameter>(element::f32, shape);
      auto f = make_shared<Function>(make_shared<op::GeluBackpropFactor>(A), ParameterVector{A});
  
      auto backend = runtime::Backend::create("${BACKEND_NAME}");
  
      // Create some tensors for input/output
      auto a = backend->create_tensor(element::f32, shape);
      vector<float> input{-4.0f, -3.0f, -2.0f, -1.0f, 0.0f, 1.0f, 2.0f, 3.0f};
      copy_data(a, input);
      auto result = backend->create_tensor(element::f32, shape);
  
      std::transform(input.begin(), input.end(), input.begin(), [](float x) -> float {
          return static_cast<float>(gelu_backprop_factor(static_cast<double>(x)));
      });
  
      auto handle = backend->compile(f);
      handle->call_with_validate({result}, {a});
      EXPECT_TRUE(
          test::all_close_f(input, read_vector<float>(result), DEFAULT_FLOAT_TOLERANCE_BITS + 6));
  }
  
  NGRAPH_TEST(${BACKEND_NAME}, gelu_backprop_factor_f64)
  {
      Shape shape{8};
      auto A = make_shared<op::Parameter>(element::f64, shape);
      auto f = make_shared<Function>(make_shared<op::GeluBackpropFactor>(A), ParameterVector{A});
  
      auto backend = runtime::Backend::create("${BACKEND_NAME}");
  
      // Create some tensors for input/output
      auto a = backend->create_tensor(element::f64, shape);
      vector<double> input{-4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0};
      copy_data(a, input);
      auto result = backend->create_tensor(element::f64, shape);
  
      std::transform(input.begin(), input.end(), input.begin(), [](double x) -> double {
          return gelu_backprop_factor(x);
      });
  
      auto handle = backend->compile(f);
      handle->call_with_validate({result}, {a});
      EXPECT_TRUE(test::all_close_f(input, read_vector<double>(result)));
  }
  
  NGRAPH_TEST(${BACKEND_NAME}, backwards_gelu_f32)
  {
      auto backend = runtime::Backend::create("${BACKEND_NAME}");
  
      Shape shape{8};
      auto make_graph = [shape]() {
          auto A = make_shared<op::Parameter>(element::f32, shape);
          return make_shared<Function>(make_shared<op::Gelu>(A), ParameterVector{A});
      };
  
      // Create some tensors for input/output
      auto a = backend->create_tensor(element::f32, shape);
      vector<float> input{-4.0f, -3.0f, -2.0f, -1.0f, 0.0f, 1.0f, 2.0f, 3.0f};
      copy_data(a, input);
  
      EXPECT_TRUE(autodiff_numeric_compare<float>(backend.get(), make_graph, {a}, .01f, .01f));
  }
  
  NGRAPH_TEST(${BACKEND_NAME}, backwards_gelu_f64)
  {
      auto backend = runtime::Backend::create("${BACKEND_NAME}");
  
      Shape shape{8};
      auto make_graph = [shape]() {
          auto A = make_shared<op::Parameter>(element::f64, shape);
          return make_shared<Function>(make_shared<op::Gelu>(A), ParameterVector{A});
      };
  
      // Create some tensors for input/output
      auto a = backend->create_tensor(element::f64, shape);
      vector<double> input{-4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0};
      copy_data(a, input);
  
      EXPECT_TRUE(autodiff_numeric_compare<double>(backend.get(), make_graph, {a}, .01, .01));
  }
  