code: def multi_predict(tracks, kalman_filter):
      if len(tracks) > 0:
          multi_mean = np.asarray(
              [track.mean.copy() for track in tracks]
          )
          multi_covariance = np.asarray(
              [track.covariance for track in tracks]
          )
          for i, st in enumerate(tracks):
              if st.state != TrackState.Tracked:
                  multi_mean[i][7] = 0
          multi_mean, multi_covariance = kalman_filter.multi_predict(
              multi_mean, multi_covariance
          )
          for i, (mean, cov) in enumerate(
              zip(multi_mean, multi_covariance)
          ):
              tracks[i].mean = mean
              tracks[i].covariance = cov
  
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 4 for calling the STrack obj's underlying member method code with example usage: obj.multi_predict(tracks, kalman_filter) without return types