class_docstr: Interpolate tracks to make tracks more complete.
  
  Args:
      min_num_frames (int, optional): The minimum length of a track that will
          be interpolated. Defaults to 5.
      max_num_frames (int, optional): The maximum disconnected length in
          a track. Defaults to 20.
      use_gsi (bool, optional): Whether to use the GSI (Gaussian-smoothed
          interpolation) method. Defaults to False.
      smooth_tau (int, optional): smoothing parameter in GSI. Defaults to 10.
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docstr: Gaussian-Smoothed Interpolation.
  
  This function is proposed in
  "StrongSORT: Make DeepSORT Great Again"
  `StrongSORT<https://arxiv.org/abs/2202.13514>`_.
  
  Args:
      track (ndarray): With shape (N, 7). Each row denotes
          (frame_id, track_id, x1, y1, x2, y2, score).
      smooth_tau (int, optional): smoothing parameter in GSI.
          Defaults to 10.
  
  Returns:
      ndarray: The interpolated tracks with shape (N, 7). Each row
          denotes (frame_id, track_id, x1, y1, x2, y2, score)
docstr_tok: ["Gaussian-Smoothed","Interpolation",".","This","function","is","proposed","in","''","StrongSORT",":","Make","DeepSORT","Great","Again","''","`","StrongSORT","<","https",":","\/\/arxiv.org\/abs\/2202.13514",">","`","_",".","Args",":","track","(","ndarray",")",":","With","shape","(","N",",","7",")",".","Each","row","denotes","(","frame_id",",","track_id",",","x1",",","y1",",","x2",",","y2",",","score",")",".","smooth_tau","(","int",",","optional",")",":","smoothing","parameter","in","GSI",".","Defaults","to","10",".","Returns",":","ndarray",":","The","interpolated","tracks","with","shape","(","N",",","7",")",".","Each","row","denotes","(","frame_id",",","track_id",",","x1",",","y1",",","x2",",","y2",",","score",")"]
code: def gaussian_smoothed_interpolation(
      self, track: np.ndarray, smooth_tau: int = 10
  ) -> np.ndarray:
      """Gaussian-Smoothed Interpolation.
  
      This function is proposed in
      "StrongSORT: Make DeepSORT Great Again"
      `StrongSORT<https://arxiv.org/abs/2202.13514>`_.
  
      Args:
          track (ndarray): With shape (N, 7). Each row denotes
              (frame_id, track_id, x1, y1, x2, y2, score).
          smooth_tau (int, optional): smoothing parameter in GSI.
              Defaults to 10.
  
      Returns:
          ndarray: The interpolated tracks with shape (N, 7). Each row
              denotes (frame_id, track_id, x1, y1, x2, y2, score)
      """
      len_scale = np.clip(
          smooth_tau * np.log(smooth_tau**3 / len(track)),
          smooth_tau**-1,
          smooth_tau**2,
      )
      gpr = GPR(RBF(len_scale, "fixed"))
      t = track[:, 0].reshape(-1, 1)
      x1 = track[:, 2].reshape(-1, 1)
      y1 = track[:, 3].reshape(-1, 1)
      x2 = track[:, 4].reshape(-1, 1)
      y2 = track[:, 5].reshape(-1, 1)
      gpr.fit(t, x1)
      x1_gpr = gpr.predict(t)
      gpr.fit(t, y1)
      y1_gpr = gpr.predict(t)
      gpr.fit(t, x2)
      x2_gpr = gpr.predict(t)
      gpr.fit(t, y2)
      y2_gpr = gpr.predict(t)
      gsi_track = [
          [
              t[i, 0],
              track[i, 1],
              x1_gpr[i],
              y1_gpr[i],
              x2_gpr[i],
              y2_gpr[i],
              track[i, 6],
          ]
          for i in range(len(t))
      ]
      return np.array(gsi_track)
  
imports: import numpy
  from mmdet.registry import TASK_UTILS
label_desc: Use image node_id 3 for calling the InterpolateTracklets obj's underlying member method code with example usage: obj.gaussian_smoothed_interpolation(track, smooth_tau) and returns: np