docstr: Args:
      input: NCHW images
      rois: Bx5 boxes. First column is the index into N. The other 4 columns are xyxy.
docstr_tok: ["Args",":","input",":","NCHW","images","rois",":","Bx5","boxes",".","First","column","is","the","index","into","N.","The","other","4","columns","are","xyxy","."]
code: def forward(self, input, rois):
      """
      Args:
          input: NCHW images
          rois: Bx5 boxes. First column is the index into N. The other 4 columns are xyxy.
      """
      assert rois.dim() == 2 and rois.size(1) == 5
      if input.is_quantized:
          input = input.dequantize()
      return roi_align(
          input,
          rois.to(dtype=input.dtype),
          self.output_size,
          self.spatial_scale,
          self.sampling_ratio,
          self.aligned,
      )
  
imports: from torch import nn
  from torchvision.ops import roi_align
label_desc: Use image node_id 2 for calling the ROIAlign obj's underlying member method code with example usage: obj.forward(input, rois) and returns: roi_align