code: def draw_predictions(task, video_vis):
      """
      Draw prediction for the given task.
      Args:
          task (TaskInfo object): task object that contain
              the necessary information for visualization. (e.g. frames, preds)
              All attributes must lie on CPU devices.
          video_vis (VideoVisualizer object): the video visualizer object.
      """
      boxes = task.bboxes
      frames = task.frames
      preds = task.action_preds
      if boxes is not None:
          img_width = task.img_width
          img_height = task.img_height
          if boxes.device != torch.device("cpu"):
              boxes = boxes.cpu()
          boxes = cv2_transform.revert_scaled_boxes(
              task.crop_size, boxes, img_height, img_width
          )
  
      keyframe_idx = len(frames) // 2 - task.num_buffer_frames
      draw_range = [
          keyframe_idx - task.clip_vis_size,
          keyframe_idx + task.clip_vis_size,
      ]
      buffer = frames[: task.num_buffer_frames]
      frames = frames[task.num_buffer_frames :]
      if boxes is not None:
          if len(boxes) != 0:
              frames = video_vis.draw_clip_range(
                  frames,
                  preds,
                  boxes,
                  keyframe_idx=keyframe_idx,
                  draw_range=draw_range,
              )
      else:
          frames = video_vis.draw_clip_range(
              frames,
              preds,
              keyframe_idx=keyframe_idx,
              draw_range=draw_range,
          )
      del task
  
      return buffer + frames
  
imports: import atexit
  import numpy
  import queue
  import torch
  import torch.multiprocessing
  import slowfast.utils.logging
  from slowfast.datasets import cv2_transform
  from slowfast.visualization.predictor import Predictor
label_desc: Use image node_id 1 for calling a global function with example usage: draw_predictions(task, video_vis) and returns: unknown