code: def _nn_euclidean_distance(s, q):
      """
      Compute pair-wise squared (Euclidean) distance between points in `s` and `q`.
  
      Args:
          s (ndarray): Sample points: an NxM matrix of N samples of dimensionality M.
          q (ndarray): Query points: an LxM matrix of L samples of dimensionality M.
  
      Returns:
          distances (ndarray): A vector of length M that contains for each entry in `q` the
              smallest Euclidean distance to a sample in `s`.
      """
      s, q = np.asarray(s), np.asarray(q)
      if len(s) == 0 or len(q) == 0:
          return np.zeros((len(s), len(q)))
      s2, q2 = np.square(s).sum(axis=1), np.square(q).sum(axis=1)
      distances = -2.0 * np.dot(s, q.T) + s2[:, None] + q2[None, :]
      distances = np.clip(distances, 0.0, float(np.inf))
  
      return np.maximum(0.0, distances.min(axis=0))
  
file_path: PaddleHub/modules/video/multiple_object_tracking/jde_darknet53/modeling/mot/matching/deepsort_matching.py
imports: import numpy
  from scipy.optimize import linear_sum_assignment
  from ..motion import kalman_filter
label_desc: Use image node_id 3 for calling a global function with example usage: _nn_euclidean_distance(s, q) and returns: np, np