  static bool Run(OpKernelContext* ctx, const Tensor& input,
                  const Tensor& filter, int batch, int input_rows,
                  int input_cols, int in_depth, int filter_rows,
                  int filter_cols, int pad_rows, int pad_cols, int out_rows,
                  int out_cols, int out_depth, int dilation_rows,
                  int dilation_cols, int stride_rows, int stride_cols,
                  Tensor* output, TensorFormat data_format) {
    if (data_format != FORMAT_NHWC || dilation_rows != 1 ||
        dilation_cols != 1 ||
        !CanUseDeepConv2D(stride_rows, stride_cols, filter_rows, filter_cols,
                          in_depth, out_depth, out_rows, out_cols)) {
      return false;
    }

    Conv2DArgs args;
    args.batch = batch;
    args.in_rows = input_rows;
    args.in_cols = input_cols;
    args.in_depth = in_depth;
    args.filter_rows = filter_rows;
    args.filter_cols = filter_cols;
    args.pad_rows = pad_rows;
    args.pad_cols = pad_cols;
    args.out_rows = out_rows;
    args.out_cols = out_cols;
    args.out_depth = out_depth;

    auto input_ptr = input.template flat<float>().data();
    auto filter_ptr = filter.template flat<float>().data();
    auto output_ptr = output->template flat<float>().data();

    functor::DeepConv2D<CPUDevice, float>()(ctx, args, input_ptr, filter_ptr,
                                            output_ptr);
    return true;
  }