def METHOD_NAME(self):
    # resnet_tsm with depth 50, no pretrained, use temporal_pool
    resnet_tsm_50_temporal_pool = ResNetTSM(
        50, temporal_pool=True, pretrained='torchvision://resnet50')
    resnet_tsm_50_temporal_pool.init_weights()
    for layer_name in resnet_tsm_50_temporal_pool.res_layers:
        layer = getattr(resnet_tsm_50_temporal_pool, layer_name)
        blocks = list(layer.children())
        if layer_name == 'layer2':
            assert len(blocks) == 2
            assert isinstance(blocks[1], nn.MaxPool3d)
            blocks = copy.deepcopy(blocks[0])
        for block in blocks:
            assert isinstance(block.conv1.conv, TemporalShift)
            if layer_name == 'layer1':
                assert block.conv1.conv.num_segments == \
                    resnet_tsm_50_temporal_pool.num_segments
            else:
                assert block.conv1.conv.num_segments == \
                    resnet_tsm_50_temporal_pool.num_segments // 2
            assert block.conv1.conv.shift_div == resnet_tsm_50_temporal_pool.shift_div  # noqa: E501
            assert isinstance(block.conv1.conv.net, nn.Conv2d)
    feat = resnet_tsm_50_temporal_pool(self.imgs)
    assert feat.shape == torch.Size([4, 2048, 2, 2])