text: // Copyright 2018 The AITS DNNC Authors.All Rights Reserved.
  //
  // Licensed to the Apache Software Foundation(ASF) under one
  // or more contributor license agreements.See the NOTICE file
  // distributed with this work for additional information
  // regarding copyright ownership.The ASF licenses this file
  // to you 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 permissionsand limitations
  // under the License.
  //
  // This file is part of AITS DNN compiler maintained at
  // https://github.com/ai-techsystems/dnnCompiler
  //
  
  #pragma once
  #include "operators/baseOperator.h"
  #include <string>
  
  using namespace Eigen;
  
  namespace dnnc {
  template <typename T> class Equal : public baseOperator<T> {
  public:
    Equal(std::string name = "opEqual") : baseOperator<T>(opEqual, name) {}
  
    tensor<bool> compute(tensor<T> a, tensor<T> b) {
  
      std::vector<DIMENSION> resultShape = binaryBroadcastReShape(a, b);
      tensor<bool> result(resultShape);
  
      if (a.shape() != b.shape())
        throw std::invalid_argument(
            "tensor dimenions not appropriate for Equal operator.");
  
      for (size_t i = 0; i < a.length(); i++)
        result[i] = a[i] == b[i] ? true : false;
  
      return result;
    }
  };
  } // namespace dnnc
  