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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _objective_function(self, data_align, data_sup, labels, w, s, theta, bias):
 """Compute the objective function of the Semi-Supervised SRM See :eq:`sssrm-eq`. Parameters data_align : list of 2D arrays, element i has shape=[voxels_i, n_align] Each element in the list contains the fMRI data for alignment of one subject. There are n_align samples for each subject. data_sup : 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 data samples in data_sup. w : list of array, element i has shape=[voxels_i, features] The orthogonal transforms (mappings) :math:`W_i` for each subject. s : array, shape=[features, samples] The shared response. theta : array, shape=[classes, features] The MLR class plane parameters. bias : array, shape=[classes] The MLR class biases. Returns ------- f_val : float The SS-SRM objective function evaluated based on the parameters to this function. """

        subjects = len(data_align)

        # Compute the SRM  loss
        f_val = 0.0
        for subject in range(subjects):
            samples = data_align[subject].shape[1]
            f_val += (1 - self.alpha) * (0.5 / samples) \
                * np.linalg.norm(data_align[subject] - w[subject].dot(s),
                                 'fro')**2

        # Compute the MLR loss
        f_val += self._loss_lr(data_sup, labels, w, theta, bias)

        return f_val