void Compute(OpKernelContext* context) override {
    const Tensor& images = context->input(0);
    const Tensor& boxes = context->input(1);
    const int64 depth = images.dim_size(3);

    OP_REQUIRES(context, images.dims() == 4,
                errors::InvalidArgument("The rank of the images should be 4"));
    OP_REQUIRES(
        context, boxes.dims() == 3,
        errors::InvalidArgument("The rank of the boxes tensor should be 3"));
    OP_REQUIRES(context, images.dim_size(0) == boxes.dim_size(0),
                errors::InvalidArgument("The batch sizes should be the same"));

    OP_REQUIRES(
        context, depth == 4 || depth == 1 || depth == 3,
        errors::InvalidArgument("Channel depth should be either 1 (GRY), "
                                "3 (RGB), or 4 (RGBA)"));

    const int64 batch_size = images.dim_size(0);
    const int64 height = images.dim_size(1);
    const int64 width = images.dim_size(2);
    std::vector<std::vector<float>> color_table;
    if (context->num_inputs() == 3) {
      const Tensor& colors_tensor = context->input(2);
      OP_REQUIRES(context, colors_tensor.shape().dims() == 2,
                  errors::InvalidArgument("colors must be a 2-D matrix",
                                          colors_tensor.shape().DebugString()));
      OP_REQUIRES(context, colors_tensor.shape().dim_size(1) >= depth,
                  errors::InvalidArgument("colors must have equal or more ",
                                          "channels than the image provided: ",
                                          colors_tensor.shape().DebugString()));
      if (colors_tensor.NumElements() != 0) {
        color_table.clear();

        auto colors = colors_tensor.matrix<float>();
        for (int64 i = 0; i < colors.dimension(0); i++) {
          std::vector<float> color_value(4);
          for (int64 j = 0; j < 4; j++) {
            color_value[j] = colors(i, j);
          }
          color_table.emplace_back(color_value);
        }
      }
    }
    if (color_table.empty()) {
      color_table = DefaultColorTable(depth);
    }
    Tensor* output;
    OP_REQUIRES_OK(
        context,
        context->allocate_output(
            0, TensorShape({batch_size, height, width, depth}), &output));

    output->tensor<T, 4>() = images.tensor<T, 4>();
    auto canvas = output->tensor<T, 4>();

    for (int64 b = 0; b < batch_size; ++b) {
      const int64 num_boxes = boxes.dim_size(1);
      const auto tboxes = boxes.tensor<T, 3>();
      for (int64 bb = 0; bb < num_boxes; ++bb) {
        int64 color_index = bb % color_table.size();
        const int64 min_box_row =
            static_cast<float>(tboxes(b, bb, 0)) * (height - 1);
        const int64 min_box_row_clamp = std::max<int64>(min_box_row, int64{0});
        const int64 max_box_row =
            static_cast<float>(tboxes(b, bb, 2)) * (height - 1);
        const int64 max_box_row_clamp =
            std::min<int64>(max_box_row, height - 1);
        const int64 min_box_col =
            static_cast<float>(tboxes(b, bb, 1)) * (width - 1);
        const int64 min_box_col_clamp = std::max<int64>(min_box_col, int64{0});
        const int64 max_box_col =
            static_cast<float>(tboxes(b, bb, 3)) * (width - 1);
        const int64 max_box_col_clamp = std::min<int64>(max_box_col, width - 1);

        if (min_box_row > max_box_row || min_box_col > max_box_col) {
          LOG(WARNING) << "Bounding box (" << min_box_row << "," << min_box_col
                       << "," << max_box_row << "," << max_box_col
                       << ") is inverted and will not be drawn.";
          continue;
        }
        if (min_box_row >= height || max_box_row < 0 || min_box_col >= width ||
            max_box_col < 0) {
          LOG(WARNING) << "Bounding box (" << min_box_row << "," << min_box_col
                       << "," << max_box_row << "," << max_box_col
                       << ") is completely outside the image"
                       << " and will not be drawn.";
          continue;
        }

        // At this point, {min,max}_box_{row,col}_clamp are inside the
        // image.
        CHECK_GE(min_box_row_clamp, 0);
        CHECK_GE(max_box_row_clamp, 0);
        CHECK_LT(min_box_row_clamp, height);
        CHECK_LT(max_box_row_clamp, height);
        CHECK_GE(min_box_col_clamp, 0);
        CHECK_GE(max_box_col_clamp, 0);
        CHECK_LT(min_box_col_clamp, width);
        CHECK_LT(max_box_col_clamp, width);

        // At this point, the min_box_row and min_box_col are either
        // in the image or above/left of it, and max_box_row and
        // max_box_col are either in the image or below/right or it.
        CHECK_LT(min_box_row, height);
        CHECK_GE(max_box_row, 0);
        CHECK_LT(min_box_col, width);
        CHECK_GE(max_box_col, 0);

        // Draw top line.
        if (min_box_row >= 0) {
          for (int64 j = min_box_col_clamp; j <= max_box_col_clamp; ++j)
            for (int64 c = 0; c < depth; c++) {
              canvas(b, min_box_row, j, c) =
                  static_cast<T>(color_table[color_index][c]);
            }
        }
        // Draw bottom line.
        if (max_box_row < height) {
          for (int64 j = min_box_col_clamp; j <= max_box_col_clamp; ++j)
            for (int64 c = 0; c < depth; c++) {
              canvas(b, max_box_row, j, c) =
                  static_cast<T>(color_table[color_index][c]);
            }
        }
        // Draw left line.
        if (min_box_col >= 0) {
          for (int64 i = min_box_row_clamp; i <= max_box_row_clamp; ++i)
            for (int64 c = 0; c < depth; c++) {
              canvas(b, i, min_box_col, c) =
                  static_cast<T>(color_table[color_index][c]);
            }
        }
        // Draw right line.
        if (max_box_col < width) {
          for (int64 i = min_box_row_clamp; i <= max_box_row_clamp; ++i)
            for (int64 c = 0; c < depth; c++) {
              canvas(b, i, max_box_col, c) =
                  static_cast<T>(color_table[color_index][c]);
            }
        }
      }
    }
  }