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 "itt.hpp"
  
  #include "ngraph/op/divide.hpp"
  #include "ngraph/op/log.hpp"
  
  #include "ngraph/runtime/host_tensor.hpp"
  #include "ngraph/runtime/reference/log.hpp"
  
  using namespace std;
  using namespace ngraph;
  
  constexpr NodeTypeInfo op::Log::type_info;
  
  op::Log::Log(const Output<Node>& arg)
      : UnaryElementwiseArithmetic(arg)
  {
      constructor_validate_and_infer_types();
  }
  
  bool ngraph::op::v0::Log::visit_attributes(AttributeVisitor& visitor)
  {
      NGRAPH_OP_SCOPE(v0_Log_visit_attributes);
      return true;
  }
  
  shared_ptr<Node> op::Log::clone_with_new_inputs(const OutputVector& new_args) const
  {
      NGRAPH_OP_SCOPE(v0_Log_clone_with_new_inputs);
      check_new_args_count(this, new_args);
      return make_shared<Log>(new_args.at(0));
  }
  
  namespace logop
  {
      template <element::Type_t ET>
      inline bool evaluate(const HostTensorPtr& arg0, const HostTensorPtr& out, const size_t count)
      {
          using T = typename element_type_traits<ET>::value_type;
          runtime::reference::log<T>(arg0->get_data_ptr<ET>(), out->get_data_ptr<ET>(), count);
          return true;
      }
  
      bool evaluate_log(const HostTensorPtr& arg0, const HostTensorPtr& out, const size_t count)
      {
          bool rc = true;
          out->set_unary(arg0);
  
          switch (arg0->get_element_type())
          {
              NGRAPH_TYPE_CASE(evaluate_log, boolean, arg0, out, count);
              NGRAPH_TYPE_CASE(evaluate_log, i32, arg0, out, count);
              NGRAPH_TYPE_CASE(evaluate_log, i64, arg0, out, count);
              NGRAPH_TYPE_CASE(evaluate_log, u32, arg0, out, count);
              NGRAPH_TYPE_CASE(evaluate_log, u64, arg0, out, count);
              NGRAPH_TYPE_CASE(evaluate_log, f16, arg0, out, count);
              NGRAPH_TYPE_CASE(evaluate_log, f32, arg0, out, count);
          default: rc = false; break;
          }
          return rc;
      }
  }
  
  bool op::Log::evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const
  {
      NGRAPH_OP_SCOPE(v0_Log_evaluate);
      return logop::evaluate_log(inputs[0], outputs[0], shape_size(get_output_shape(0)));
  }
  