text: #include "roi_align.h"
  
  #include <torch/types.h>
  
  namespace vision {
  namespace ops {
  
  at::Tensor roi_align(
      const at::Tensor& input, // Input feature map.
      const at::Tensor& rois, // List of ROIs to pool over.
      double spatial_scale, // The scale of the image features. ROIs will be
      // scaled to this.
      int64_t pooled_height, // The height of the pooled feature map.
      int64_t pooled_width, // The width of the pooled feature
      int64_t sampling_ratio, // The number of points to sample in each bin
      bool aligned) // The flag for pixel shift
  // along each axis.
  {
    C10_LOG_API_USAGE_ONCE("torchvision.csrc.ops.roi_align.roi_align");
    static auto op = c10::Dispatcher::singleton()
                         .findSchemaOrThrow("torchvision::roi_align", "")
                         .typed<decltype(roi_align)>();
    return op.call(
        input,
        rois,
        spatial_scale,
        pooled_height,
        pooled_width,
        sampling_ratio,
        aligned);
  }
  
  namespace detail {
  
  at::Tensor _roi_align_backward(
      const at::Tensor& grad,
      const at::Tensor& rois,
      double spatial_scale,
      int64_t pooled_height,
      int64_t pooled_width,
      int64_t batch_size,
      int64_t channels,
      int64_t height,
      int64_t width,
      int64_t sampling_ratio,
      bool aligned) {
    static auto op =
        c10::Dispatcher::singleton()
            .findSchemaOrThrow("torchvision::_roi_align_backward", "")
            .typed<decltype(_roi_align_backward)>();
    return op.call(
        grad,
        rois,
        spatial_scale,
        pooled_height,
        pooled_width,
        batch_size,
        channels,
        height,
        width,
        sampling_ratio,
        aligned);
  }
  
  } // namespace detail
  
  TORCH_LIBRARY_FRAGMENT(torchvision, m) {
    m.def(TORCH_SELECTIVE_SCHEMA(
        "torchvision::roi_align(Tensor input, Tensor rois, float spatial_scale, int pooled_height, int pooled_width, int sampling_ratio, bool aligned) -> Tensor"));
    m.def(TORCH_SELECTIVE_SCHEMA(
        "torchvision::_roi_align_backward(Tensor grad, Tensor rois, float spatial_scale, int pooled_height, int pooled_width, int batch_size, int channels, int height, int width, int sampling_ratio, bool aligned) -> Tensor"));
  }
  
  } // namespace ops
  } // namespace vision
  