class_docstr: This module implements the task- and model-specific estimator for PyTorch GOTURN (object tracking).
class_docstr_tok: ["This","module","implements","the","task-","and","model-specific","estimator","for","PyTorch","GOTURN","(","object","tracking",")","."]
docstr: Method `update` for GOT-10k trackers.
  
  :param image: Current image.
  :return: Predicted box.
docstr_tok: ["Method","`","update","`","for","GOT-10k","trackers",".",":","param","image",":","Current","image",".",":","return",":","Predicted","box","."]
code: def update(self, image: np.ndarray) -> np.ndarray:
      """
      Method `update` for GOT-10k trackers.
  
      :param image: Current image.
      :return: Predicted box.
      """
      import torch
  
      curr = torch.from_numpy(np.array(image) / 255.0)
      if self.clip_values is not None:
          curr = curr * self.clip_values[1]
      curr = curr.to(self.device)
      prev = torch.from_numpy(self.prev).to(self.device)
  
      curr, _ = self._apply_preprocessing(curr, y=None, fit=False)
  
      self.box = self._track_step(curr, prev, self.box)
      self.prev = curr.cpu().detach().numpy()
  
      box_return = self.box.cpu().detach().numpy()
      box_return = np.array(
          [
              box_return[0],
              box_return[1],
              box_return[2] - box_return[0],
              box_return[3] - box_return[1],
          ]
      )
  
      return box_return
  
imports: import logging
  import time
  from typing import List, Dict, Optional, Tuple, Union, TYPE_CHECKING
  import numpy
  from art.estimators.object_tracking.object_tracker import ObjectTrackerMixin
  from art.estimators.pytorch import PyTorchEstimator
label_desc: Use image node_id 16 for calling the PyTorchGoturn obj's underlying member method code with example usage: obj.update(image) and returns: box_return