code: def detector_postprocess(
      results, output_height, output_width, mask_threshold=0.5
  ):
      """
      Resize the output instances.
      The input images are often resized when entering an object detector.
      As a result, we often need the outputs of the detector in a different
      resolution from its inputs.
  
      This function will resize the raw outputs of an R-CNN detector
      to produce outputs according to the desired output resolution.
  
      Args:
          results (Instances): the raw outputs from the detector.
              `results.image_size` contains the input image resolution the detector sees.
              This object might be modified in-place.
          output_height, output_width: the desired output resolution.
  
      Returns:
          Instances: the resized output from the model, based on the output resolution
      """
  
      # Converts integer tensors to float temporaries
      #   to ensure true division is performed when
      #   computing scale_x and scale_y.
      if isinstance(output_width, torch.Tensor):
          output_width_tmp = output_width.float()
      else:
          output_width_tmp = output_width
  
      if isinstance(output_height, torch.Tensor):
          output_height_tmp = output_height.float()
      else:
          output_height_tmp = output_height
  
      scale_x, scale_y = (
          output_width_tmp / results.image_size[1],
          output_height_tmp / results.image_size[0],
      )
      results = Instances(
          (output_height, output_width), **results.get_fields()
      )
  
      if results.has("pred_boxes"):
          output_boxes = results.pred_boxes
      elif results.has("proposal_boxes"):
          output_boxes = results.proposal_boxes
  
      output_boxes.scale(scale_x, scale_y)
      output_boxes.clip(results.image_size)
  
      results = results[output_boxes.nonempty()]
  
      if results.has("pred_masks"):
          results.pred_masks = retry_if_cuda_oom(paste_masks_in_image)(
              results.pred_masks[:, 0, :, :],  # N, 1, M, M
              results.pred_boxes,
              results.image_size,
              threshold=mask_threshold,
          )
  
      if results.has("pred_keypoints"):
          results.pred_keypoints[:, :, 0] *= scale_x
          results.pred_keypoints[:, :, 1] *= scale_y
  
      return results
  
imports: import torch
  from torch import nn
  from ...data.structures import Instances
  from ...utils.utils import retry_if_cuda_oom
label_desc: Use image node_id 2 for calling a global function with example usage: detector_postprocess(results, output_height, output_width, mask_threshold) and returns: results