def METHOD_NAME(self):
    non_local_cfg = dict(
        sub_sample=True,
        use_scale=False,
        norm_cfg=dict(type='BN3d', requires_grad=True),
        mode='embedded_gaussian')
    non_local = ((0, 0, 0), (1, 0, 1, 0), (1, 0, 1, 0, 1, 0), (0, 0, 0))
    resnet_tsm_50_full = ResNetTSM(
        50,
        pretrained='torchvision://resnet50',
        non_local=non_local,
        non_local_cfg=non_local_cfg,
        temporal_pool=True)
    resnet_tsm_50_full.init_weights()
    input_shape = (16, 3, 32, 32)
    imgs = generate_backbone_demo_inputs(input_shape)
    feat = resnet_tsm_50_full(imgs)
    assert feat.shape == torch.Size([8, 2048, 1, 1])