How to use the deeplabcut.pose_estimation_tensorflow.nnet.net_factory.pose_net function in deeplabcut

def setup_pose_prediction(cfg):
    TF.reset_default_graph()
    inputs = TF.placeholder(tf.float32, shape=[cfg.batch_size   , None, None, 3])
    net_heads = pose_net(cfg).test(inputs)
    outputs = [net_heads['part_prob']]
    if cfg.location_refinement:
        outputs.append(net_heads['locref'])

    restorer = TF.train.Saver()
    sess = TF.Session()
    sess.run(TF.global_variables_initializer())
    sess.run(TF.local_variables_initializer())

    # Restore variables from disk.
    restorer.restore(sess, cfg.init_weights)

    return sess, inputs, outputs

def setup_pose_prediction(cfg):
    TF.reset_default_graph()
    inputs = TF.placeholder(tf.float32, shape=[cfg.batch_size   , None, None, 3])
    net_heads = pose_net(cfg).test(inputs)
    outputs = [net_heads['part_prob']]
    if cfg.location_refinement:
        outputs.append(net_heads['locref'])
    if cfg.pairwise_predict:
        outputs.append(net_heads['pairwise_pred'])

    restorer = TF.train.Saver()
    sess = TF.Session()
    sess.run(TF.global_variables_initializer())
    sess.run(TF.local_variables_initializer())

    # Restore variables from disk.
    restorer.restore(sess, cfg.init_weights)

    return sess, inputs, outputs

def setup_GPUpose_prediction(self):
        ''' Adapted from DLC '''
        TF.reset_default_graph()
        inputs = TF.placeholder(TF.float32, shape=[self.cfg.batch_size, None, None, 3])
        net_heads = pose_net(self.cfg).inference(inputs)
        outputs = [net_heads['pose']]

        restorer = TF.train.Saver()
        sess = TF.Session()

        sess.run(TF.global_variables_initializer())
        sess.run(TF.local_variables_initializer())

        # Restore variables from disk.
        restorer.restore(sess, self.cfg.init_weights)

        return sess, inputs, outputs

def train(config_yaml,displayiters,saveiters,maxiters,max_to_keep=5,keepdeconvweights=True,allow_growth=False):
    start_path=os.getcwd()
    os.chdir(str(Path(config_yaml).parents[0])) #switch to folder of config_yaml (for logging)
    setup_logging()

    cfg = load_config(config_yaml)
    if cfg.dataset_type=='default' or cfg.dataset_type=='tensorpack' or cfg.dataset_type=='deterministic':
        print("Switching batchsize to 1, as default/tensorpack/deterministic loaders do not support batches >1. Use imgaug loader.")
        cfg['batch_size']=1 #in case this was edited for analysis.-

    dataset = create_dataset(cfg)
    batch_spec = get_batch_spec(cfg)
    batch, enqueue_op, placeholders = setup_preloading(batch_spec)
    losses = pose_net(cfg).train(batch)
    total_loss = losses['total_loss']

    for k, t in losses.items():
        TF.summary.scalar(k, t)
    merged_summaries = TF.summary.merge_all()

    if 'snapshot' in Path(cfg.init_weights).stem and keepdeconvweights:
        print("Loading already trained DLC with backbone:", cfg.net_type)
        variables_to_restore = slim.get_variables_to_restore()
    else:
        print("Loading ImageNet-pretrained", cfg.net_type)
        #loading backbone from ResNet, MobileNet etc.
        if 'resnet' in cfg.net_type:
            variables_to_restore = slim.get_variables_to_restore(include=["resnet_v1"])
        elif 'mobilenet' in cfg.net_type:
            variables_to_restore = slim.get_variables_to_restore(include=["MobilenetV2"])

def train(config_yaml,displayiters,saveiters,maxiters,max_to_keep=5,keepdeconvweights=True,allow_growth=False):
    start_path=os.getcwd()
    os.chdir(str(Path(config_yaml).parents[0])) #switch to folder of config_yaml (for logging)
    setup_logging()

    cfg = load_config(config_yaml)
    if cfg.dataset_type=='default' or cfg.dataset_type=='tensorpack' or cfg.dataset_type=='deterministic':
        print("Switching batchsize to 1, as default/tensorpack/deterministic loaders do not support batches >1. Use imgaug loader.")
        cfg['batch_size']=1 #in case this was edited for analysis.-

    dataset = create_dataset(cfg)
    batch_spec = get_batch_spec(cfg)
    batch, enqueue_op, placeholders = setup_preloading(batch_spec)
    losses = pose_net(cfg).train(batch)
    total_loss = losses['total_loss']

    for k, t in losses.items():
        TF.summary.scalar(k, t)
    merged_summaries = TF.summary.merge_all()

    if 'snapshot' in Path(cfg.init_weights).stem and keepdeconvweights:
        print("Loading already trained DLC with backbone:", cfg.net_type)
        variables_to_restore = slim.get_variables_to_restore()
    else:
        print("Loading ImageNet-pretrained", cfg.net_type)
        #loading backbone from ResNet, MobileNet etc.
        if 'resnet' in cfg.net_type:
            variables_to_restore = slim.get_variables_to_restore(include=["resnet_v1"])
        elif 'mobilenet' in cfg.net_type:
            variables_to_restore = slim.get_variables_to_restore(include=["MobilenetV2"])

def setup_GPUpose_prediction(cfg):
    tf.reset_default_graph()
    inputs = tf.placeholder(tf.float32, shape=[cfg.batch_size   , None, None, 3])
    net_heads = pose_net(cfg).inference(inputs)
    outputs = [net_heads['pose']]

    restorer = tf.train.Saver()
    sess = tf.Session()

    sess.run(tf.global_variables_initializer())
    sess.run(tf.local_variables_initializer())

    # Restore variables from disk.
    restorer.restore(sess, cfg.init_weights)

    return sess, inputs, outputs