code: def predict(self):
      mean_state = self.mean.copy()
      if self.state != TrackState.Tracked:
          mean_state[7] = 0
      self.mean, self.covariance = self.kalman_filter.predict(
          mean_state, self.covariance
      )
  
imports: import numpy
  from collections import defaultdict
  from collections import deque, OrderedDict
  from ..matching import jde_matching
  from ppdet.core.workspace import register, serializable
  import warnings
label_desc: Use image node_id 3 for calling the STrack obj's underlying member method code with example usage: obj.predict() without return types