class_docstr: Action Predictor for action recognition.
class_docstr_tok: ["Action","Predictor","for","action","recognition","."]
docstr: Returns the prediction results for the current task.
  Args:
      task (TaskInfo object): task object that contain
          the necessary information for action prediction. (e.g. frames, boxes)
  Returns:
      task (TaskInfo object): the same task info object but filled with
          prediction values (a tensor) and the corresponding boxes for
          action detection task.
docstr_tok: ["Returns","the","prediction","results","for","the","current","task",".","Args",":","task","(","TaskInfo","object",")",":","task","object","that","contain","the","necessary","information","for","action","prediction",".","(","e.g",".","frames",",","boxes",")","Returns",":","task","(","TaskInfo","object",")",":","the","same","task","info","object","but","filled","with","prediction","values","(","a","tensor",")","and","the","corresponding","boxes","for","action","detection","task","."]
code: def __call__(self, task):
      """
      Returns the prediction results for the current task.
      Args:
          task (TaskInfo object): task object that contain
              the necessary information for action prediction. (e.g. frames, boxes)
      Returns:
          task (TaskInfo object): the same task info object but filled with
              prediction values (a tensor) and the corresponding boxes for
              action detection task.
      """
      if self.cfg.DETECTION.ENABLE:
          task = self.object_detector(task)
  
      frames, bboxes = task.frames, task.bboxes
      if bboxes is not None:
          bboxes = cv2_transform.scale_boxes(
              self.cfg.DATA.TEST_CROP_SIZE,
              bboxes,
              task.img_height,
              task.img_width,
          )
      if self.cfg.DEMO.INPUT_FORMAT == "BGR":
          frames = [
              cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) for frame in frames
          ]
  
      frames = [
          cv2_transform.scale(self.cfg.DATA.TEST_CROP_SIZE, frame)
          for frame in frames
      ]
      inputs = process_cv2_inputs(frames, self.cfg)
      if bboxes is not None:
          index_pad = torch.full(
              size=(bboxes.shape[0], 1),
              fill_value=float(0),
              device=bboxes.device,
          )
  
          # Pad frame index for each box.
          bboxes = torch.cat([index_pad, bboxes], axis=1)
      if self.cfg.NUM_GPUS > 0:
          # Transfer the data to the current GPU device.
          if isinstance(inputs, (list,)):
              for i in range(len(inputs)):
                  inputs[i] = inputs[i].cuda(
                      device=torch.device(self.gpu_id),
                      non_blocking=True,
                  )
          else:
              inputs = inputs.cuda(
                  device=torch.device(self.gpu_id), non_blocking=True
              )
      if self.cfg.DETECTION.ENABLE and not bboxes.shape[0]:
          preds = torch.tensor([])
      else:
          preds = self.model(inputs, bboxes)
  
      if self.cfg.NUM_GPUS:
          preds = preds.cpu()
          if bboxes is not None:
              bboxes = bboxes.detach().cpu()
  
      preds = preds.detach()
      task.add_action_preds(preds)
      if bboxes is not None:
          task.add_bboxes(bboxes[:, 1:])
  
      return task
  
imports: import queue
  import cv2
  import torch
  from detectron2 import model_zoo
  from detectron2.config import get_cfg
  from detectron2.engine import DefaultPredictor
  import slowfast.utils.checkpoint
  from slowfast.datasets import cv2_transform
  from slowfast.models import build_model
  from slowfast.utils import logging
  from slowfast.visualization.utils import process_cv2_inputs
label_desc: Use image node_id 2 for calling the Predictor obj's underlying member method code with example usage: obj.__call__(task) and returns: task