def METHOD_NAME(self):
    decoder_cfg = dict(
        type='IntegralRegressionLabel',
        input_size=(192, 256),
        heatmap_size=(48, 64),
        sigma=2)
    head = DSNTHead(
        in_channels=32,
        in_featuremap_size=(6, 8),
        num_joints=17,
        decoder=decoder_cfg,
    )
    feats = self._get_feats(batch_size=2, feat_shapes=[(32, 8, 6)])
    batch_data_samples = get_packed_inputs(
        batch_size=2, with_reg_label=False)['data_samples']
    preds = head.predict(feats, batch_data_samples)
    self.assertTrue(len(preds), 2)
    self.assertIsInstance(preds[0], InstanceData)
    self.assertEqual(preds[0].keypoints.shape,
                     batch_data_samples[0].gt_instances.keypoints.shape)
    # output heatmap
    head = DSNTHead(
        in_channels=32,
        in_featuremap_size=(6, 8),
        num_joints=17,
        decoder=decoder_cfg,
    )
    feats = self._get_feats(batch_size=2, feat_shapes=[(32, 8, 6)])
    batch_data_samples = get_packed_inputs(
        batch_size=2, with_reg_label=False)['data_samples']
    _, pred_heatmaps = head.predict(
        feats, batch_data_samples, test_cfg=dict(output_heatmaps=True))
    self.assertTrue(len(pred_heatmaps), 2)
    self.assertIsInstance(pred_heatmaps[0], PixelData)
    self.assertEqual(pred_heatmaps[0].heatmaps.shape, (17, 8 * 8, 6 * 8))