code: def ps_roi_pool(
      input: Tensor,
      boxes: Tensor,
      output_size: int,
      spatial_scale: float = 1.0,
  ) -> Tensor:
      """
      Performs Position-Sensitive Region of Interest (RoI) Pool operator
      described in R-FCN
  
      Args:
          input (Tensor[N, C, H, W]): The input tensor, i.e. a batch with ``N`` elements. Each element
              contains ``C`` feature maps of dimensions ``H x W``.
          boxes (Tensor[K, 5] or List[Tensor[L, 4]]): the box coordinates in (x1, y1, x2, y2)
              format where the regions will be taken from.
              The coordinate must satisfy ``0 <= x1 < x2`` and ``0 <= y1 < y2``.
              If a single Tensor is passed, then the first column should
              contain the index of the corresponding element in the batch, i.e. a number in ``[0, N - 1]``.
              If a list of Tensors is passed, then each Tensor will correspond to the boxes for an element i
              in the batch.
          output_size (int or Tuple[int, int]): the size of the output (in bins or pixels) after the pooling
              is performed, as (height, width).
          spatial_scale (float): a scaling factor that maps the box coordinates to
              the input coordinates. For example, if your boxes are defined on the scale
              of a 224x224 image and your input is a 112x112 feature map (resulting from a 0.5x scaling of
              the original image), you'll want to set this to 0.5. Default: 1.0
  
      Returns:
          Tensor[K, C / (output_size[0] * output_size[1]), output_size[0], output_size[1]]: The pooled RoIs.
      """
      if not torch.jit.is_scripting() and not torch.jit.is_tracing():
          _log_api_usage_once(ps_roi_pool)
      _assert_has_ops()
      check_roi_boxes_shape(boxes)
      rois = boxes
      output_size = _pair(output_size)
      if not isinstance(rois, torch.Tensor):
          rois = convert_boxes_to_roi_format(rois)
      output, _ = torch.ops.torchvision.ps_roi_pool(
          input, rois, spatial_scale, output_size[0], output_size[1]
      )
      return output
  
file_path: vision/torchvision/ops/ps_roi_pool.py
imports: import torch
  import torch.fx
  from torch import nn, Tensor
  from torch.nn.modules.utils import _pair
  from torchvision.extension import _assert_has_ops
  from ..utils import _log_api_usage_once
  from ._utils import check_roi_boxes_shape, convert_boxes_to_roi_format
label_desc: Use image node_id 1 for calling a global function with example usage: ps_roi_pool(input, boxes, output_size, spatial_scale) and returns: output