class_docstr: DeepSORT tracker
  
  Args:
      input_size (list): input feature map size to reid model, [h, w] format,
          [64, 192] as default.
      min_box_area (int): min box area to filter out low quality boxes
      vertical_ratio (float): w/h, the vertical ratio of the bbox to filter
          bad results, set 1.6 default for pedestrian tracking. If set <=0
          means no need to filter bboxes.
      budget (int): If not None, fix samples per class to at most this number.
          Removes the oldest samples when the budget is reached.
      max_age (int): maximum number of missed misses before a track is deleted
      n_init (float): Number of frames that a track remains in initialization
          phase. Number of consecutive detections before the track is confirmed. 
          The track state is set to `Deleted` if a miss occurs within the first 
          `n_init` frames.
      metric_type (str): either "euclidean" or "cosine", the distance metric 
          used for measurement to track association.
      matching_threshold (float): samples with larger distance are 
          considered an invalid match.
      max_iou_distance (float): max iou distance threshold
      motion (object): KalmanFilter instance
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docstr: Propagate track state distributions one time step forward.
  This function should be called once every time step, before `update`.
docstr_tok: ["Propagate","track","state","distributions","one","time","step","forward",".","This","function","should","be","called","once","every","time","step",",","before","`","update","`","."]
code: def predict(self):
      """
      Propagate track state distributions one time step forward.
      This function should be called once every time step, before `update`.
      """
      for track in self.tracks:
          track.predict(self.motion)
  
file_path: PaddleDetection/ppdet/modeling/mot/tracker/deepsort_tracker.py
imports: import numpy
  from ..motion import KalmanFilter
  from ..matching.deepsort_matching import NearestNeighborDistanceMetric
  from ..matching.deepsort_matching import iou_cost, min_cost_matching, matching_cascade, gate_cost_matrix
  from .base_sde_tracker import Track
  from ..utils import Detection
  from ppdet.core.workspace import register, serializable
  from ppdet.utils.logger import setup_logger
label_desc: Use image node_id 2 for calling the DeepSORTTracker obj's underlying member method code with example usage: obj.predict() without return types