inline void TransposeConvV2(
    const ConvParams& params, const RuntimeShape& input_shape,
    const float* input_data, const RuntimeShape& hwoi_ordered_filter_shape,
    const float* hwoi_ordered_filter_data, const RuntimeShape& bias_shape,
    const float* bias_data, const RuntimeShape& output_shape,
    float* const output_data, const RuntimeShape& col2im_shape,
    float* col2im_data, CpuBackendContext* cpu_backend_context) {
  ruy::profiler::ScopeLabel label("TransposeConvV2/float");
  TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4);
  TFLITE_DCHECK_EQ(hwoi_ordered_filter_shape.DimensionsCount(), 4);
  TFLITE_DCHECK(col2im_data);
  TFLITE_DCHECK(hwoi_ordered_filter_data);

  const int batch_size = MatchingDim(input_shape, 0, output_shape, 0);
  const int input_image_size = input_shape.Dims(1) * input_shape.Dims(2);
  const int output_height = output_shape.Dims(1);
  const int output_width = output_shape.Dims(2);
  const int output_image_size = output_height * output_width;
  const int input_depth =
      MatchingDim(input_shape, 3, hwoi_ordered_filter_shape, 3);
  const int output_depth =
      MatchingDim(output_shape, 3, hwoi_ordered_filter_shape, 2);
  const int input_offset = input_image_size * input_depth;
  const int output_offset = output_image_size * output_depth;

  const int filter_height = hwoi_ordered_filter_shape.Dims(0);
  const int filter_width = hwoi_ordered_filter_shape.Dims(1);
  const int padding_top = params.padding_values.height;
  const int padding_bottom =
      params.padding_values.height + params.padding_values.height_offset;
  const int padding_left = params.padding_values.width;
  const int padding_right =
      params.padding_values.width + params.padding_values.width_offset;
  const int stride_height = params.stride_height;
  const int stride_width = params.stride_width;

  const int hwoi_ordered_filter_total_size =
      filter_height * filter_width * output_depth;

  cpu_backend_gemm::MatrixParams<float> lhs_params;
  lhs_params.order = cpu_backend_gemm::Order::kRowMajor;
  lhs_params.rows = hwoi_ordered_filter_total_size;
  lhs_params.cols = input_depth;
  float* output_data_p = output_data;
  std::fill_n(output_data, output_offset * batch_size, 0.0f);
  for (int i = 0; i < batch_size; ++i) {
    cpu_backend_gemm::MatrixParams<float> rhs_params;
    rhs_params.order = cpu_backend_gemm::Order::kColMajor;
    rhs_params.rows = input_depth;
    rhs_params.cols = input_image_size;
    cpu_backend_gemm::MatrixParams<float> dst_params;
    dst_params.order = cpu_backend_gemm::Order::kColMajor;
    dst_params.rows = hwoi_ordered_filter_total_size;
    dst_params.cols = input_image_size;
    cpu_backend_gemm::GemmParams<float, float> gemm_params;
    cpu_backend_gemm::Gemm(lhs_params, hwoi_ordered_filter_data, rhs_params,
                           input_data + input_offset * i, dst_params,
                           col2im_data, gemm_params, cpu_backend_context);

    Col2im(col2im_data, output_depth, output_height, output_width,
           filter_height, filter_width, padding_top, padding_left,
           padding_bottom, padding_right, stride_height, stride_width,
           output_data_p);
    output_data_p += output_offset;
  }
  output_data_p = output_data;
  BiasAdd(output_data_p, bias_data, batch_size, output_height, output_width,
          output_depth);
}
inline void TransposeConvV2(
    const ConvParams& params, const RuntimeShape& input_shape,
    const float* input_data, const RuntimeShape& hwoi_ordered_filter_shape,
    const float* hwoi_ordered_filter_data, const RuntimeShape& bias_shape,
    const float* bias_data, const RuntimeShape& output_shape,
    float* const output_data, const RuntimeShape& col2im_shape,
    float* col2im_data, CpuBackendContext* cpu_backend_context) {
  ruy::profiler::ScopeLabel label("TransposeConvV2/float");
  TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4);
  TFLITE_DCHECK_EQ(hwoi_ordered_filter_shape.DimensionsCount(), 4);
  TFLITE_DCHECK(col2im_data);
  TFLITE_DCHECK(hwoi_ordered_filter_data);

  const int batch_size = MatchingDim(input_shape, 0, output_shape, 0);
  const int input_image_size = input_shape.Dims(1) * input_shape.Dims(2);
  const int output_height = output_shape.Dims(1);
  const int output_width = output_shape.Dims(2);
  const int output_image_size = output_height * output_width;
  const int input_depth =
      MatchingDim(input_shape, 3, hwoi_ordered_filter_shape, 3);
  const int output_depth =
      MatchingDim(output_shape, 3, hwoi_ordered_filter_shape, 2);
  const int input_offset = input_image_size * input_depth;
  const int output_offset = output_image_size * output_depth;

  const int filter_height = hwoi_ordered_filter_shape.Dims(0);
  const int filter_width = hwoi_ordered_filter_shape.Dims(1);
  const int padding_top = params.padding_values.height;
  const int padding_bottom =
      params.padding_values.height + params.padding_values.height_offset;
  const int padding_left = params.padding_values.width;
  const int padding_right =
      params.padding_values.width + params.padding_values.width_offset;
  const int stride_height = params.stride_height;
  const int stride_width = params.stride_width;

  const int hwoi_ordered_filter_total_size =
      filter_height * filter_width * output_depth;

  cpu_backend_gemm::MatrixParams<float> lhs_params;
  lhs_params.order = cpu_backend_gemm::Order::kRowMajor;
  lhs_params.rows = hwoi_ordered_filter_total_size;
  lhs_params.cols = input_depth;
  float* output_data_p = output_data;
  std::fill_n(output_data, output_offset * batch_size, 0.0f);
  for (int i = 0; i < batch_size; ++i) {
    cpu_backend_gemm::MatrixParams<float> rhs_params;
    rhs_params.order = cpu_backend_gemm::Order::kColMajor;
    rhs_params.rows = input_depth;
    rhs_params.cols = input_image_size;
    cpu_backend_gemm::MatrixParams<float> dst_params;
    dst_params.order = cpu_backend_gemm::Order::kColMajor;
    dst_params.rows = hwoi_ordered_filter_total_size;
    dst_params.cols = input_image_size;
    cpu_backend_gemm::GemmParams<float, float> gemm_params;
    cpu_backend_gemm::Gemm(lhs_params, hwoi_ordered_filter_data, rhs_params,
                           input_data + input_offset * i, dst_params,
                           col2im_data, gemm_params, cpu_backend_context);

    Col2im(col2im_data, output_depth, output_height, output_width,
           filter_height, filter_width, padding_top, padding_left,
           padding_bottom, padding_right, stride_height, stride_width,
           output_data_p);
    output_data_p += output_offset;
  }
  output_data_p = output_data;
  BiasAdd(output_data_p, bias_data, batch_size, output_height, output_width,
          output_depth);
}