def METHOD_NAME(model):
    is_train = False
    if model.training:
        model.eval()
        is_train = True
    fuse_list = []
    tmp_pair = [None, None]
    for name, layer in model.named_sublayers():
        if isinstance(layer, nn.Conv2D):
            tmp_pair[0] = name
        if isinstance(layer, nn.BatchNorm2D):
            tmp_pair[1] = name
        if tmp_pair[0] and tmp_pair[1] and len(tmp_pair) == 2:
            fuse_list.append(tmp_pair)
            tmp_pair = [None, None]
    model = fuse_layers(model, fuse_list)
    if is_train:
        model.train()
    return model