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 <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/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}, acosh)
  {
      Shape shape{11};
      auto A = make_shared<op::Parameter>(element::f32, shape);
      auto f = make_shared<Function>(make_shared<op::Acosh>(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{0.f, 1.f, -1.f, 2.f, -2.f, 3.f, -3.f, 4.f, 5.f, 10.f, 100.f};
      copy_data(a, input);
      auto result = backend->create_tensor(element::f32, shape);
      auto handle = backend->compile(f);
      handle->call_with_validate({result}, {a});
      vector<float> expected;
      for (float f : input)
      {
          expected.push_back(std::acosh(f));
      }
      EXPECT_TRUE(test::all_close_f(expected, read_vector<float>(result)));
  }
  