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 <memory>
  
  #include "ngraph/op/convert.hpp"
  #include "ngraph/op/less.hpp"
  #include "ngraph/op/minimum.hpp"
  #include "ngraph/op/multiply.hpp"
  #include "ngraph/runtime/host_tensor.hpp"
  #include "ngraph/runtime/reference/minimum.hpp"
  #include "ngraph/type/element_type.hpp"
  
  using namespace std;
  using namespace ngraph;
  
  namespace
  {
      template <element::Type_t ET>
      bool evaluate(const HostTensorPtr& arg0,
                    const HostTensorPtr& arg1,
                    const HostTensorPtr& out,
                    const op::AutoBroadcastSpec& broadcast_spec)
      {
          runtime::reference::minimum(arg0->get_data_ptr<ET>(),
                                      arg1->get_data_ptr<ET>(),
                                      out->get_data_ptr<ET>(),
                                      arg0->get_shape(),
                                      arg1->get_shape(),
                                      broadcast_spec);
          return true;
      }
  
      bool evaluate_minimum(const HostTensorPtr& arg0,
                            const HostTensorPtr& arg1,
                            const HostTensorPtr& out,
                            const op::AutoBroadcastSpec& broadcast_spec)
      {
          bool rc = true;
          out->set_broadcast(broadcast_spec, arg0, arg1);
          switch (arg0->get_element_type())
          {
              TYPE_CASE(i8)(arg0, arg1, out, broadcast_spec);
              break;
              TYPE_CASE(i16)(arg0, arg1, out, broadcast_spec);
              break;
              TYPE_CASE(i32)(arg0, arg1, out, broadcast_spec);
              break;
              TYPE_CASE(i64)(arg0, arg1, out, broadcast_spec);
              break;
              TYPE_CASE(u8)(arg0, arg1, out, broadcast_spec);
              break;
              TYPE_CASE(u16)(arg0, arg1, out, broadcast_spec);
              break;
              TYPE_CASE(u32)(arg0, arg1, out, broadcast_spec);
              break;
              TYPE_CASE(u64)(arg0, arg1, out, broadcast_spec);
              break;
              TYPE_CASE(f32)(arg0, arg1, out, broadcast_spec);
              break;
              TYPE_CASE(f64)(arg0, arg1, out, broadcast_spec);
              break;
          default: rc = false; break;
          }
          return rc;
      }
  }
  
  constexpr NodeTypeInfo op::v1::Minimum::type_info;
  
  op::v1::Minimum::Minimum(const Output<Node>& arg0,
                           const Output<Node>& arg1,
                           const AutoBroadcastSpec& auto_broadcast)
      : BinaryElementwiseArithmetic(arg0, arg1, auto_broadcast)
  {
      constructor_validate_and_infer_types();
  }
  
  shared_ptr<Node> op::v1::Minimum::clone_with_new_inputs(const OutputVector& new_args) const
  {
      check_new_args_count(this, new_args);
      return make_shared<op::v1::Minimum>(new_args.at(0), new_args.at(1), this->get_autob());
  }
  
  void op::v1::Minimum::generate_adjoints(autodiff::Adjoints& adjoints, const OutputVector& deltas)
  {
      bool static_shapes =
          input(0).get_partial_shape().is_static() && input(1).get_partial_shape().is_static();
      if (static_shapes && input(0).get_shape() == input(1).get_shape())
      {
          // It does not matter if broadcast is enabled since shapes match
      }
      else if (get_autob().m_type != op::AutoBroadcastType::NONE)
      {
          throw ngraph_error("Autodiff not supported with auto broadcasting");
      }
  
      auto delta = deltas.at(0);
  
      auto x = input_value(0);
      auto y = input_value(1);
  
      adjoints.add_delta(x,
                         delta * make_shared<op::v0::Convert>(make_shared<op::v1::Less>(x, y),
                                                              x.get_element_type()));
      adjoints.add_delta(y,
                         delta * make_shared<op::v0::Convert>(make_shared<op::v1::Less>(y, x),
                                                              y.get_element_type()));
  }
  
  bool op::v1::Minimum::evaluate(const HostTensorVector& outputs,
                                 const HostTensorVector& inputs) const
  {
      return evaluate_minimum(inputs[0], inputs[1], outputs[0], get_autob());
  }
  