//  [python]
def per_train(self):

        self.model.train()
        train_loader = self.data_loader['train']
        batch_loss = 0.
        N = 0.

        for joint_offsets, pos, affs, quat, quat_valid_idx,\
            text, text_valid_idx, intended_emotion, intended_polarity,\
                acting_task, gender, age, handedness,\
                native_tongue in self.yield_batch(self.args.batch_size, train_loader):
            quat_prelude = self.quats_eos.view(1, -1).cuda() \
                .repeat(self.T - 1, 1).unsqueeze(0).repeat(quat.shape[0], 1, 1).float()
            quat_prelude[:, -1] = quat[:, 0].clone()

            self.optimizer.zero_grad()
            with torch.autograd.detect_anomaly():
                joint_lengths = torch.norm(joint_offsets, dim=-1)
                scales, _ = torch.max(joint_lengths, dim=-1)
                quat_pred, quat_pred_pre_norm = self.model(text, intended_emotion, intended_polarity,
                                                           acting_task, gender, age, handedness, native_tongue,
                                                           quat_prelude, joint_lengths / scales[..., None])

                quat_pred_pre_norm = quat_pred_pre_norm.view(quat_pred_pre_norm.shape[0],
                                                             quat_pred_pre_norm.shape[1], -1, self.D)
                quat_norm_loss = self.args.quat_norm_reg *\
                                 torch.mean((torch.sum(quat_pred_pre_norm ** 2, dim=-1) - 1) ** 2)

                quat_loss, quat_derv_loss = losses.quat_angle_loss(quat_pred, quat[:, 1:],
                                                                   quat_valid_idx[:, 1:],
                                                                   self.V, self.D,
                                                                   self.lower_body_start,
                                                                   self.args.upper_body_weight)
                quat_loss *= self.args.quat_reg

                root_pos = torch.zeros(quat_pred.shape[0], quat_pred.shape[1], self.C).cuda()
                pos_pred = MocapDataset.forward_kinematics(quat_pred.contiguous().view(
                    quat_pred.shape[0], quat_pred.shape[1], -1, self.D), root_pos, self.joint_parents,
                    torch.cat((root_pos[:, 0:1], joint_offsets), dim=1).unsqueeze(1))
                affs_pred = MocapDataset.get_mpi_affective_features(pos_pred)

                # row_sums = quat_valid_idx.sum(1, keepdim=True) * self.D * self.V
                # row_sums[row_sums == 0.] = 1.

                shifted_pos = pos - pos[:, :, 0:1]
                shifted_pos_pred = pos_pred - pos_pred[:, :, 0:1]

                recons_loss = self.recons_loss_func(shifted_pos_pred, shifted_pos[:, 1:])
                # recons_loss = torch.abs(shifted_pos_pred - shifted_pos[:, 1:]).sum(-1)
                # recons_loss = self.args.upper_body_weight * (recons_loss[:, :, :self.lower_body_start].sum(-1)) +\
                #               recons_loss[:, :, self.lower_body_start:].sum(-1)
                # recons_loss = self.args.recons_reg *\
                #               torch.mean((recons_loss * quat_valid_idx[:, 1:]).sum(-1) / row_sums)
                #
                # recons_derv_loss = torch.abs(shifted_pos_pred[:, 1:] - shifted_pos_pred[:, :-1] -
                #                              shifted_pos[:, 2:] + shifted_pos[:, 1:-1]).sum(-1)
                # recons_derv_loss = self.args.upper_body_weight *\
                #     (recons_derv_loss[:, :, :self.lower_body_start].sum(-1)) +\
                #                    recons_derv_loss[:, :, self.lower_body_start:].sum(-1)
                # recons_derv_loss = 2. * self.args.recons_reg *\
                #                    torch.mean((recons_derv_loss * quat_valid_idx[:, 2:]).sum(-1) / row_sums)
                #
                # affs_loss = torch.abs(affs[:, 1:] - affs_pred).sum(-1)
                # affs_loss = self.args.affs_reg * torch.mean((affs_loss * quat_valid_idx[:, 1:]).sum(-1) / row_sums)
                affs_loss = self.affs_loss_func(affs_pred, affs[:, 1:])

                train_loss = quat_norm_loss + quat_loss + recons_loss + affs_loss
                # train_loss = quat_norm_loss + quat_loss + recons_loss + recons_derv_loss + affs_loss
                train_loss.backward()
                # nn.utils.clip_grad_norm_(self.model.parameters(), self.args.gradient_clip)
                self.optimizer.step()

            # animation_pred = {
            #     'joint_names': self.joint_names,
            #     'joint_offsets': joint_offsets,
            #     'joint_parents': self.joint_parents,
            #     'positions': pos_pred,
            #     'rotations': quat_pred
            # }
            # MocapDataset.save_as_bvh(animation_pred,
            #                          dataset_name=self.dataset,
            #                          subset_name='test')
            # animation = {
            #     'joint_names': self.joint_names,
            #     'joint_offsets': joint_offsets,
            #     'joint_parents': self.joint_parents,
            #     'positions': pos,
            #     'rotations': quat
            # }
            # MocapDataset.save_as_bvh(animation,
            #                          dataset_name=self.dataset,
            #                          subset_name='gt')

            # Compute statistics
            batch_loss += train_loss.item()
            N += quat.shape[0]

            # statistics
            self.iter_info['loss'] = train_loss.data.item()
            self.iter_info['lr'] = '{:.6f}'.format(self.lr)
            self.iter_info['tf'] = '{:.6f}'.format(self.tf)
            self.show_iter_info()
            self.meta_info['iter'] += 1

        batch_loss = batch_loss / N
        self.epoch_info['mean_loss'] = batch_loss
        self.show_epoch_info()
        self.io.print_timer()
        self.adjust_lr()
        self.adjust_tf()