code: def boss(x, y):
      r"""Return the BOSS distance between two arrays.
  
      Parameters
      ----------
  
      x : array-like, shape = (n_timestamps,)
          First array.
  
      y : array-like, shape = (n_timestamps,)
          Second array.
  
      Returns
      -------
      dist : float
          The BOSS distance between both arrays.
  
      Notes
      -----
      The BOSS metric is defined as
  
      .. math::
  
          BOSS(x, y) = \sum_{\substack{i=1\\ x_i > 0}}^n (x_i - y_i)^2
  
      where :math:`x` and :math:`y` are vectors of non-negative integers.
      The BOSS distance is not a distance metric as it neither satisfies the
      symmetry condition nor the triangle inequality.
  
      References
      ----------
      .. [1] P. Schäfer, "The BOSS is concerned with time series classification
             in the presence of noise". Data Mining and Knowledge Discovery,
             29(6), 1505-1530 (2015).
  
      Examples
      --------
      >>> from pyts.metrics import boss
      >>> x = [0, 5, 5, 3, 4, 5]
      >>> y = [3, 0, 0, 0, 8, 0]
      >>> boss(x, y)
      10.0
      >>> boss(y, x)
      5.0
  
      """
      x = check_array(x, ensure_2d=False, dtype="float64")
      y = check_array(y, ensure_2d=False, dtype="float64")
      if x.ndim != 1:
          raise ValueError("'x' must a one-dimensional array.")
      if y.ndim != 1:
          raise ValueError("'y' must a one-dimensional array.")
      if x.shape != y.shape:
          raise ValueError("'x' and 'y' must have the same shape.")
  
      non_zero_idx = ~np.isclose(
          x, np.zeros_like(x), rtol=1e-5, atol=1e-8
      )
      return sqrt(np.sum((x[non_zero_idx] - y[non_zero_idx]) ** 2))
  
label_desc: Use image node_id 1 for calling a global function with example usage: boss(x, y) and returns: sqrt