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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_subject(self, data_align, data_sup, labels, w, s, theta, bias):
 """Compute the objective function for one subject. .. math:: (1-C)*Loss_{SRM}_i(W_i,S;X_i) .. math:: + C/\\gamma * Loss_{MLR_i}(\\theta, bias; {(W_i^T*Z_i, y_i}) .. math:: + R(\\theta) Parameters data_align : 2D array, shape=[voxels_i, samples_align] Contains the fMRI data for alignment of subject i. data_sup : 2D array, shape=[voxels_i, samples_i] Contains the fMRI data of one subject for the classification task. labels : array of int, shape=[samples_i] The labels for the data samples in data_sup. w : array, shape=[voxels_i, features] The orthogonal transform (mapping) :math:`W_i` for subject i. 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 for subject i evaluated on the parameters to this function. """

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

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

        return f_val