code: def roi_align(
      input: Tensor,
      boxes: Union[Tensor, List[Tensor]],
      output_size: BroadcastingList2[int],
      spatial_scale: float = 1.0,
      sampling_ratio: int = -1,
      aligned: bool = False,
  ) -> Tensor:
      """
      Performs Region of Interest (RoI) Align operator with average pooling, as described in Mask R-CNN.
  
      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``.
              If the tensor is quantized, we expect a batch size of ``N == 1``.
          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
          sampling_ratio (int): number of sampling points in the interpolation grid
              used to compute the output value of each pooled output bin. If > 0,
              then exactly ``sampling_ratio x sampling_ratio`` sampling points per bin are used. If
              <= 0, then an adaptive number of grid points are used (computed as
              ``ceil(roi_width / output_width)``, and likewise for height). Default: -1
          aligned (bool): If False, use the legacy implementation.
              If True, pixel shift the box coordinates it by -0.5 for a better alignment with the two
              neighboring pixel indices. This version is used in Detectron2
  
      Returns:
          Tensor[K, C, 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(roi_align)
      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)
      if not torch.jit.is_scripting():
          if not _has_ops() or (
              torch.are_deterministic_algorithms_enabled()
              and (input.is_cuda or input.is_mps)
          ):
              return _roi_align(
                  input,
                  rois,
                  spatial_scale,
                  output_size[0],
                  output_size[1],
                  sampling_ratio,
                  aligned,
              )
      _assert_has_ops()
      return torch.ops.torchvision.roi_align(
          input,
          rois,
          spatial_scale,
          output_size[0],
          output_size[1],
          sampling_ratio,
          aligned,
      )
  
file_path: vision/torchvision/ops/roi_align.py
imports: from typing import List, Union
  import torch
  import torch._dynamo
  import torch.fx
  from torch import nn, Tensor
  from torch.jit.annotations import BroadcastingList2
  from torch.nn.modules.utils import _pair
  from torchvision.extension import _assert_has_ops, _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 4 for calling a global function with example usage: roi_align(input, boxes, output_size, spatial_scale, sampling_ratio, aligned) and returns: torch, _roi_align