code: def pure_nugget(theta, d):
      """
      Spatial independence correlation model (pure nugget).
      (Useful when one wants to solve an ordinary least squares problem!)::
  
                                             n
          theta, d --> r(theta, d) = 1 if   sum |d_i| == 0
                                           i = 1
                                     0 otherwise
  
      Parameters
      ----------
      theta : array_like
          None.
  
      d : array_like
          An array with shape (n_eval, n_features) giving the componentwise
          distances between locations x and x' at which the correlation model
          should be evaluated.
  
      Returns
      -------
      r : array_like
          An array with shape (n_eval, ) with the values of the autocorrelation
          model.
      """
  
      theta = np.asarray(theta, dtype=np.float64)
      d = np.asarray(d, dtype=np.float64)
  
      n_eval = d.shape[0]
      r = np.zeros(n_eval)
      r[np.all(d == 0.0, axis=1)] = 1.0
  
      return r
  
imports: import numpy
label_desc: Use image node_id 4 for calling a global function with example usage: pure_nugget(theta, d) and returns: r