def METHOD_NAME(self):
    codec = dict(
        type='MSRAHeatmap',
        input_size=(192, 256),
        heatmap_size=(48, 64),
        sigma=2.)
    head = VisPredictHead(
        pose_cfg=dict(
            type='HeatmapHead',
            in_channels=32,
            out_channels=17,
            deconv_out_channels=None,
            loss=dict(type='KeypointMSELoss', use_target_weight=True),
            decoder=codec))
    feats = self._get_feats(batch_size=2, feat_shapes=[(32, 128, 128)])
    batch_data_samples = get_packed_inputs(batch_size=2)['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)
    self.assertEqual(
        preds[0].keypoint_scores.shape,
        batch_data_samples[0].gt_instance_labels.keypoint_weights.shape)
    # output heatmap
    head = VisPredictHead(
        pose_cfg=dict(
            type='HeatmapHead',
            in_channels=32,
            out_channels=17,
            decoder=codec))
    feats = self._get_feats(batch_size=2, feat_shapes=[(32, 8, 6)])
    batch_data_samples = get_packed_inputs(batch_size=2)['data_samples']
    _, pred_heatmaps = head.predict(
        feats, batch_data_samples, test_cfg=dict(output_heatmaps=True))
    self.assertIsInstance(pred_heatmaps[0], PixelData)
    self.assertEqual(pred_heatmaps[0].heatmaps.shape, (17, 64, 48))