code: def _loss_lr(self, data, labels, w, theta, bias):
          
          subjects = len(data)
          loss = 0.0
          for subject in range(subjects):
              if labels[subject] is not None:
                  loss += self._loss_lr_subject(data[subject], labels[subject],
                                                w[subject], theta, bias)
  
          return loss + 0.5 * np.linalg.norm(theta, 'fro')**2
docs: Compute the Loss MLR (with the regularization)
  
          Parameters
          ----------
  
          data : list of 2D arrays, element i has shape=[voxels_i, samples_i]
              Each element in the list contains the fMRI data of one subject for
              the classification task.
  
          labels : list of arrays of int, element i has shape=[samples_i]
              Each element in the list contains the labels for the samples in
              data.
  
          w : list of array, element i has shape=[voxels_i, features]
              The orthogonal transforms (mappings) :math:`W_i` for each subject.
  
          theta : array, shape=[classes, features]
              The MLR class plane parameters.
  
          bias : array, shape=[classes]
              The MLR class biases.
  
  
          Returns
          -------
  
          loss : float
              The loss MLR for the SS-SRM model