code: def _numba_euclidean_distance(x: np.ndarray, y: np.ndarray) -> float:
      """Euclidean distance compiled to no_python.
  
      Parameters
      ----------
      x: np.ndarray (2d array shape (d,m))
          First time series.
      y: np.ndarray (2d array shape (d,m))
          Second time series.
  
      Returns
      -------
      distance: float
          Euclidean distance between x and y.
      """
      distance = 0.0
      for i in range(x.shape[0]):
          for j in range(x.shape[1]):
              difference = x[i][j] - y[i][j]
              distance += difference * difference
      return np.sqrt(distance)
  
label_desc: Use image node_id 2 for calling a global function with example usage: _numba_euclidean_distance(x, y) and returns: np