text: /*
   * Copyright (c) 2018 ARM Limited.
   *
   * SPDX-License-Identifier: MIT
   *
   * Permission is hereby granted, free of charge, to any person obtaining a copy
   * of this software and associated documentation files (the "Software"), to
   * deal in the Software without restriction, including without limitation the
   * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
   * sell copies of the Software, and to permit persons to whom the Software is
   * furnished to do so, subject to the following conditions:
   *
   * The above copyright notice and this permission notice shall be included in all
   * copies or substantial portions of the Software.
   *
   * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
   * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
   * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
   * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
   * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
   * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
   * SOFTWARE.
   */
  #ifndef ARM_COMPUTE_TEST_LSTM_LAYER_FIXTURE
  #define ARM_COMPUTE_TEST_LSTM_LAYER_FIXTURE
  
  #include "tests/Globals.h"
  #include "tests/framework/Asserts.h"
  #include "tests/framework/Fixture.h"
  #include "tests/validation/reference/ActivationLayer.h"
  #include "tests/validation/reference/ArithmeticAddition.h"
  #include "tests/validation/reference/ArithmeticSubtraction.h"
  #include "tests/validation/reference/FullyConnectedLayer.h"
  #include "tests/validation/reference/GEMM.h"
  #include "tests/validation/reference/PixelWiseMultiplication.h"
  #include "tests/validation/reference/Transpose.h"
  
  namespace arm_compute
  {
  namespace test
  {
  namespace validation
  {
  template <typename TensorType, typename AccessorType, typename FunctionType, typename FunctionParams, typename T>
  class LSTMLayerValidationFixture : public framework::Fixture
  {
  public:
      template <typename...>
      void setup(TensorShape input_shape, TensorShape input_weights_shape, TensorShape recurrent_weights_shape, TensorShape cell_bias_shape, TensorShape output_cell_shape, TensorShape output_shape,
                 TensorShape scratch_shape, ActivationLayerInfo info, float cell_threshold, float projection_threshold, DataType data_type, bool projection_opt, bool peephole_opt)
      {
          _target = compute_target(input_shape, input_weights_shape, recurrent_weights_shape, cell_bias_shape, output_cell_shape, output_shape, scratch_shape, info, cell_threshold, projection_threshold,
                                   data_type, projection_opt, peephole_opt);
          _reference = compute_reference(input_shape, input_weights_shape, recurrent_weights_shape, cell_bias_shape, output_cell_shape, output_shape, scratch_shape, info, cell_threshold, projection_threshold,
                                         data_type, projection_opt, peephole_opt);
      }
  
  protected:
      template <typename U>
      void fill(U &&tensor, int i)
      {
          std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
          library->fill(tensor, distribution, i);
      }
      template <typename U>
      void fill_custom_val(U &&tensor, float num, int i)
      {
          std::uniform_real_distribution<> distribution(num, num);
          library->fill(tensor, distribution, i);
      }
      TensorType compute_target(const TensorShape &input_shape, const TensorShape &input_weights_shape, const TensorShape &recurrent_weights_shape, const TensorShape &cell_bias_shape,
                                const TensorShape &output_cell_shape, const TensorShape &output_shape, const TensorShape &scratch_shape, ActivationLayerInfo info, float cell_threshold,
                                float projection_threshold, DataType data_type, bool projection_opt, bool peephole_opt)
      {
          // Create projection bias shape
          TensorShape projection_bias_shape{};
          projection_bias_shape.set(0, output_shape.x());
  
          // Create tensors
          TensorType input                 = create_tensor<TensorType>(input_shape, data_type);
          TensorType input_to_forget_w     = create_tensor<TensorType>(input_weights_shape, data_type);
          TensorType input_to_cell_w       = create_tensor<TensorType>(input_weights_shape, data_type);
          TensorType input_to_output_w     = create_tensor<TensorType>(input_weights_shape, data_type);
          TensorType recurrent_to_forget_w = create_tensor<TensorType>(recurrent_weights_shape, data_type);
          TensorType recurrent_to_cell_w   = create_tensor<TensorType>(recurrent_weights_shape, data_type);
          TensorType recurrent_to_output_w = create_tensor<TensorType>(recurrent_weights_shape, data_type);
          TensorType forget_gate_bias      = create_tensor<TensorType>(cell_bias_shape, data_type);
          TensorType cell_bias             = create_tensor<TensorType>(cell_bias_shape, data_type);
          TensorType output_gate_bias      = create_tensor<TensorType>(cell_bias_shape, data_type);
          TensorType output_state          = create_tensor<TensorType>(output_shape, data_type);
          TensorType cell_state            = create_tensor<TensorType>(output_cell_shape, data_type);
          TensorType scratch               = create_tensor<TensorType>(scratch_shape, data_type);
          TensorType output                = create_tensor<TensorType>(output_shape, data_type);
          TensorType input_to_input_w;
          TensorType recurrent_to_input_w;
          TensorType cell_to_input_w;
          TensorType cell_to_forget_w;
          TensorType input_gate_bias;
          TensorType cell_to_output_w;
          TensorType projection_w;
          TensorType projection_bias;
  
          bool cifg_opt = scratch_shape.x() == cell_bias_shape.x() * 4 ? true : false;
  
          FunctionParams lstm_params;
  
          if(!cifg_opt)
          {
              input_to_input_w     = create_tensor<TensorType>(input_weights_shape, data_type);
              recurrent_to_input_w = create_tensor<TensorType>(recurrent_weights_shape, data_type);
              cell_to_input_w      = create_tensor<TensorType>(cell_bias_shape, data_type);
              input_gate_bias      = create_tensor<TensorType>(cell_bias_shape, data_type);
              lstm_params.set_cifg_params(&input_to_input_w, &recurrent_to_input_w, &cell_to_input_w, &input_gate_bias);
          }
  
          if(peephole_opt)
          {
              if(cifg_opt)
              {
                  cell_to_input_w = create_tensor<TensorType>(cell_bias_shape, data_type);
              }
              cell_to_forget_w = create_tensor<TensorType>(cell_bias_shape, data_type);
              cell_to_output_w = create_tensor<TensorType>(cell_bias_shape, data_type);
              lstm_params.set_peephole_params(&cell_to_input_w, &cell_to_forget_w, &cell_to_output_w);
          }
  
          if(projection_opt)
          {
              projection_w    = create_tensor<TensorType>(recurrent_weights_shape, data_type);
              projection_bias = create_tensor<TensorType>(projection_bias_shape, data_type);
              lstm_params.set_projection_params(&projection_w, &projection_bias);
          }
  
          // Create and configure function
          FunctionType lstm;
          lstm.configure(&input, &input_to_forget_w, &input_to_cell_w, &input_to_output_w, &recurrent_to_forget_w,
                         &recurrent_to_cell_w, &recurrent_to_output_w, &forget_gate_bias, &cell_bias, &output_gate_bias, &output_state, &cell_state,
                         &scratch, &output, lstm_params, info, cell_threshold, projection_threshold);
  
          ARM_COMPUTE_EXPECT(input.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(input_to_forget_w.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(input_to_cell_w.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(input_to_output_w.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(recurrent_to_forget_w.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(recurrent_to_cell_w.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(recurrent_to_output_w.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(forget_gate_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(cell_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(output_gate_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(output_state.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(cell_state.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(scratch.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(output.info()->is_resizable(), framework::LogLevel::ERRORS);
  
          // Allocate tensors
          input.allocator()->allocate();
          input_to_forget_w.allocator()->allocate();
          input_to_cell_w.allocator()->allocate();
          input_to_output_w.allocator()->allocate();
          recurrent_to_forget_w.allocator()->allocate();
          recurrent_to_cell_w.allocator()->allocate();
          recurrent_to_output_w.allocator()->allocate();
          forget_gate_bias.allocator()->allocate();
          cell_bias.allocator()->allocate();
          output_gate_bias.allocator()->allocate();
          output_state.allocator()->allocate();
          cell_state.allocator()->allocate();
          scratch.allocator()->allocate();
          output.allocator()->allocate();
  
          ARM_COMPUTE_EXPECT(!input.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!input_to_forget_w.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!input_to_cell_w.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!input_to_output_w.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!recurrent_to_forget_w.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!recurrent_to_cell_w.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!recurrent_to_output_w.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!forget_gate_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!cell_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!output_gate_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!output_state.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!cell_state.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!scratch.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!output.info()->is_resizable(), framework::LogLevel::ERRORS);
  
          // Fill tensors
          fill(AccessorType(input), 0);
          fill(AccessorType(input_to_forget_w), 1);
          fill(AccessorType(input_to_cell_w), 2);
          fill(AccessorType(input_to_output_w), 3);
          fill(AccessorType(recurrent_to_forget_w), 4);
          fill(AccessorType(recurrent_to_cell_w), 5);
          fill(AccessorType(recurrent_to_output_w), 6);
          fill(AccessorType(forget_gate_bias), 7);
          fill(AccessorType(cell_bias), 8);
          fill(AccessorType(output_gate_bias), 9);
          fill(AccessorType(output_state), 10);
          fill(AccessorType(cell_state), 11);
          fill(AccessorType(scratch), 12);
  
          if(!cifg_opt)
          {
              ARM_COMPUTE_EXPECT(input_to_input_w.info()->is_resizable(), framework::LogLevel::ERRORS);
              ARM_COMPUTE_EXPECT(recurrent_to_input_w.info()->is_resizable(), framework::LogLevel::ERRORS);
              ARM_COMPUTE_EXPECT(cell_to_input_w.info()->is_resizable(), framework::LogLevel::ERRORS);
              ARM_COMPUTE_EXPECT(input_gate_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
              input_to_input_w.allocator()->allocate();
              recurrent_to_input_w.allocator()->allocate();
              cell_to_input_w.allocator()->allocate();
              input_gate_bias.allocator()->allocate();
              ARM_COMPUTE_EXPECT(!input_to_input_w.info()->is_resizable(), framework::LogLevel::ERRORS);
              ARM_COMPUTE_EXPECT(!recurrent_to_input_w.info()->is_resizable(), framework::LogLevel::ERRORS);
              ARM_COMPUTE_EXPECT(!cell_to_input_w.info()->is_resizable(), framework::LogLevel::ERRORS);
              ARM_COMPUTE_EXPECT(!input_gate_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
              fill(AccessorType(input_to_input_w), 13);
              fill(AccessorType(recurrent_to_input_w), 14);
              fill(AccessorType(cell_to_input_w), 15);
              fill(AccessorType(recurrent_to_input_w), 16);
              fill(AccessorType(input_gate_bias), 17);
          }
  
          if(peephole_opt)
          {
              if(cifg_opt)
              {
                  ARM_COMPUTE_EXPECT(cell_to_input_w.info()->is_resizable(), framework::LogLevel::ERRORS);
                  cell_to_input_w.allocator()->allocate();
                  ARM_COMPUTE_EXPECT(!cell_to_input_w.info()->is_resizable(), framework::LogLevel::ERRORS);
                  fill(AccessorType(cell_to_input_w), 15);
              }
              ARM_COMPUTE_EXPECT(cell_to_forget_w.info()->is_resizable(), framework::LogLevel::ERRORS);
              ARM_COMPUTE_EXPECT(cell_to_output_w.info()->is_resizable(), framework::LogLevel::ERRORS);
              cell_to_forget_w.allocator()->allocate();
              cell_to_output_w.allocator()->allocate();
              ARM_COMPUTE_EXPECT(!cell_to_forget_w.info()->is_resizable(), framework::LogLevel::ERRORS);
              ARM_COMPUTE_EXPECT(!cell_to_output_w.info()->is_resizable(), framework::LogLevel::ERRORS);
              fill(AccessorType(cell_to_output_w), 18);
          }
  
          if(projection_opt)
          {
              ARM_COMPUTE_EXPECT(projection_w.info()->is_resizable(), framework::LogLevel::ERRORS);
              ARM_COMPUTE_EXPECT(projection_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
  
              projection_w.allocator()->allocate();
              projection_bias.allocator()->allocate();
  
              ARM_COMPUTE_EXPECT(!projection_w.info()->is_resizable(), framework::LogLevel::ERRORS);
              ARM_COMPUTE_EXPECT(!projection_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
  
              fill(AccessorType(projection_w), 19);
              fill(AccessorType(projection_bias), 20);
          }
  
          // Compute function
          lstm.run();
  
          return output;
      }
  
      SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &input_weights_shape, const TensorShape &recurrent_weights_shape, const TensorShape &cell_bias_shape,
                                        const TensorShape &output_cell_shape, const TensorShape &output_shape, const TensorShape &scratch_shape, ActivationLayerInfo info, float cell_threshold,
                                        float projection_threshold, DataType data_type, bool projection_opt, bool peephole_opt)
      {
          // Create projection bias shape
          TensorShape projection_bias_shape{};
          projection_bias_shape.set(0, output_shape.x());
  
          TensorShape     gemm_shape{ 1, output_shape.y() };
          SimpleTensor<T> gemm_out{ gemm_shape, data_type };
  
          // Create reference
          SimpleTensor<T> input{ input_shape, data_type };
          SimpleTensor<T> input_to_input_w{ input_weights_shape, data_type };
          SimpleTensor<T> input_to_forget_w{ input_weights_shape, data_type };
          SimpleTensor<T> input_to_cell_w{ input_weights_shape, data_type };
          SimpleTensor<T> input_to_output_w{ input_weights_shape, data_type };
          SimpleTensor<T> recurrent_to_input_w{ recurrent_weights_shape, data_type };
          SimpleTensor<T> recurrent_to_forget_w{ recurrent_weights_shape, data_type };
          SimpleTensor<T> recurrent_to_cell_w{ recurrent_weights_shape, data_type };
          SimpleTensor<T> recurrent_to_output_w{ recurrent_weights_shape, data_type };
          SimpleTensor<T> cell_to_input_w{ cell_bias_shape, data_type };
          SimpleTensor<T> cell_to_forget_w{ cell_bias_shape, data_type };
          SimpleTensor<T> cell_to_output_w{ cell_bias_shape, data_type };
          SimpleTensor<T> input_gate_bias{ cell_bias_shape, data_type };
          SimpleTensor<T> forget_gate_bias{ cell_bias_shape, data_type };
          SimpleTensor<T> cell_bias{ cell_bias_shape, data_type };
          SimpleTensor<T> output_gate_bias{ cell_bias_shape, data_type };
          SimpleTensor<T> projection_w{ recurrent_weights_shape, data_type };
          SimpleTensor<T> projection_bias{ projection_bias_shape, data_type };
          SimpleTensor<T> output_state{ output_shape, data_type };
          SimpleTensor<T> cell_state{ output_cell_shape, data_type };
          SimpleTensor<T> scratch{ scratch_shape, data_type };
          SimpleTensor<T> output{ output_shape, data_type };
  
          // Fill reference
          fill(input, 0);
          fill(input_to_forget_w, 1);
          fill(input_to_cell_w, 2);
          fill(input_to_output_w, 3);
          fill(recurrent_to_forget_w, 4);
          fill(recurrent_to_cell_w, 5);
          fill(recurrent_to_output_w, 6);
          fill(forget_gate_bias, 7);
          fill(cell_bias, 8);
          fill(output_gate_bias, 9);
          fill(output_state, 10);
          fill(cell_state, 11);
          fill(scratch, 12);
          fill(input_to_input_w, 13);
          fill(recurrent_to_input_w, 14);
          fill(cell_to_input_w, 15);
          fill(recurrent_to_input_w, 16);
          fill(input_gate_bias, 17);
          fill(cell_to_output_w, 18);
          fill(projection_w, 19);
          fill(projection_bias, 20);
  
          bool cifg_opt = scratch_shape.x() == cell_bias_shape.x() * 4 ? true : false;
  
          // Compute forget_gate
          SimpleTensor<T> fully_connected_forget = reference::fully_connected_layer(input, input_to_forget_w, forget_gate_bias, output_cell_shape);
          SimpleTensor<T> transposed_weights     = reference::transpose(recurrent_to_forget_w);
          SimpleTensor<T> gemm                   = reference::gemm(output_state, transposed_weights, cell_state, 1.f, 0.f);
          SimpleTensor<T> forget_gate            = reference::arithmetic_addition(fully_connected_forget, gemm, data_type, ConvertPolicy::SATURATE);
  
          if(peephole_opt)
          {
              transposed_weights = reference::transpose(cell_to_forget_w);
              gemm               = reference::gemm(cell_state, transposed_weights, gemm_out, 1.f, 0.f);
              forget_gate        = reference::arithmetic_addition(forget_gate, gemm, data_type, ConvertPolicy::SATURATE);
          }
  
          forget_gate = reference::activation_layer(forget_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
  
          // Compute input_gate
          SimpleTensor<T> input_gate;
          if(cifg_opt)
          {
              SimpleTensor<T> ones{ cell_bias_shape, data_type };
              fill_custom_val(ones, 1.f, 0);
              input_gate = reference::arithmetic_subtraction<T, T, T>(ones, forget_gate, data_type, ConvertPolicy::SATURATE);
          }
          else
          {
              SimpleTensor<T> fully_connected_input = reference::fully_connected_layer(input, input_to_input_w, input_gate_bias, output_cell_shape);
              transposed_weights                    = reference::transpose(recurrent_to_input_w);
              gemm                                  = reference::gemm(output_state, transposed_weights, cell_state, 1.f, 0.f);
              input_gate                            = reference::arithmetic_addition(fully_connected_input, gemm, data_type, ConvertPolicy::SATURATE);
              transposed_weights                    = reference::transpose(cell_to_input_w);
              gemm                                  = reference::gemm(cell_state, transposed_weights, gemm_out, 1.f, 0.f);
              input_gate                            = reference::arithmetic_addition(input_gate, gemm, data_type, ConvertPolicy::SATURATE);
              input_gate                            = reference::activation_layer(input_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
          }
  
          // Compute cell_state
          SimpleTensor<T> fully_connected_cell_state = reference::fully_connected_layer(input, input_to_cell_w, cell_bias, output_cell_shape);
          transposed_weights                         = reference::transpose(recurrent_to_cell_w);
          gemm                                       = reference::gemm(output_state, transposed_weights, cell_state, 1.f, 0.f);
          SimpleTensor<T> pixelwise_mul              = reference::pixel_wise_multiplication(cell_state, forget_gate, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
          cell_state                                 = reference::arithmetic_addition(fully_connected_cell_state, gemm, data_type, ConvertPolicy::SATURATE);
          cell_state                                 = reference::activation_layer(cell_state, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
          cell_state                                 = reference::pixel_wise_multiplication(cell_state, input_gate, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
          cell_state                                 = reference::arithmetic_addition(cell_state, pixelwise_mul, data_type, ConvertPolicy::SATURATE);
          if(cell_threshold != 0.f)
          {
              cell_state = reference::activation_layer(cell_state, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -cell_threshold, cell_threshold));
          }
  
          // Compute output
          SimpleTensor<T> fully_connected_output = reference::fully_connected_layer(input, input_to_output_w, output_gate_bias, output_cell_shape);
          transposed_weights                     = reference::transpose(recurrent_to_output_w);
          gemm                                   = reference::gemm(output_state, transposed_weights, cell_state, 1.f, 0.f);
          output                                 = reference::arithmetic_addition(fully_connected_output, gemm, data_type, ConvertPolicy::SATURATE);
          if(peephole_opt)
          {
              transposed_weights = reference::transpose(cell_to_output_w);
              gemm               = reference::gemm(cell_state, transposed_weights, gemm_out, 1.f, 0.f);
              output             = reference::arithmetic_addition(output, gemm, data_type, ConvertPolicy::SATURATE);
          }
          output = reference::activation_layer(output, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
  
          // Compute output state
          SimpleTensor<T> cell_state_activation = reference::activation_layer(cell_state, info);
          output_state                          = reference::pixel_wise_multiplication(output, cell_state_activation, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
  
          if(projection_opt)
          {
              SimpleTensor<T> fully_connected_projection = reference::fully_connected_layer(output_state, projection_w, projection_bias, output_cell_shape);
              if(projection_threshold != 0.f)
              {
                  output_state = reference::activation_layer(fully_connected_projection, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -projection_threshold, projection_threshold));
              }
          }
          return output_state;
      }
  
      TensorType      _target{};
      SimpleTensor<T> _reference{};
  };
  } // namespace validation
  } // namespace test
  } // namespace arm_compute
  #endif /* ARM_COMPUTE_TEST_LSTM_LAYER_FIXTURE */
  