code: def LFR_optim_objective(
      parameters,
      x_unprivileged,
      x_privileged,
      y_unprivileged,
      y_privileged,
      k=10,
      A_x=0.01,
      A_y=0.1,
      A_z=0.5,
      print_interval=250,
      verbose=1,
  ):
      num_unprivileged, features_dim = x_unprivileged.shape
      num_privileged, _ = x_privileged.shape
  
      w = parameters[:k]
      prototypes = parameters[k:].reshape((k, features_dim))
  
      (
          M_unprivileged,
          x_hat_unprivileged,
          y_hat_unprivileged,
      ) = get_xhat_y_hat(prototypes, w, x_unprivileged)
  
      M_privileged, x_hat_privileged, y_hat_privileged = get_xhat_y_hat(
          prototypes, w, x_privileged
      )
  
      y_hat = np.concatenate(
          [y_hat_unprivileged, y_hat_privileged], axis=0
      )
      y = np.concatenate(
          [
              y_unprivileged.reshape((-1, 1)),
              y_privileged.reshape((-1, 1)),
          ],
          axis=0,
      )
  
      L_x = np.mean(
          (x_hat_unprivileged - x_unprivileged) ** 2
      ) + np.mean((x_hat_privileged - x_privileged) ** 2)
      L_z = np.mean(
          abs(
              np.mean(M_unprivileged, axis=0)
              - np.mean(M_privileged, axis=0)
          )
      )
      L_y = -np.mean(
          y * np.log(y_hat) + (1.0 - y) * np.log(1.0 - y_hat)
      )
  
      total_loss = A_x * L_x + A_y * L_y + A_z * L_z
  
      if verbose and LFR_optim_objective.steps % print_interval == 0:
          print(
              "step: {}, loss: {}, L_x: {},  L_y: {},  L_z: {}".format(
                  LFR_optim_objective.steps, total_loss, L_x, L_y, L_z
              )
          )
      LFR_optim_objective.steps += 1
  
      return total_loss
  
imports: import numpy
  from scipy.spatial.distance import cdist
  from scipy.special import softmax
label_desc: Use image node_id 1 for calling a global function with example usage: LFR_optim_objective(parameters, x_unprivileged, x_privileged, y_unprivileged, y_privileged, k, A_x, A_y, A_z, print_interval, verbose) and returns: total_loss