How to use the deeplabcut.pose_estimation_tensorflow.config.load_config function in deeplabcut

# Make folder for evaluation
    auxiliaryfunctions.attempttomakefolder(str(cfg["project_path"]+"/evaluation-results/"))
    for shuffle in Shuffles:
        for trainFraction in TrainingFractions:
            ##################################################
            # Load and setup CNN part detector
            ##################################################
            datafn,metadatafn=auxiliaryfunctions.GetDataandMetaDataFilenames(trainingsetfolder,trainFraction,shuffle,cfg)

            modelfolder=os.path.join(cfg["project_path"],str(auxiliaryfunctions.GetModelFolder(trainFraction,shuffle,cfg)))
            path_test_config = Path(modelfolder) / 'test' / 'pose_cfg.yaml'
            # Load meta data
            data, trainIndices, testIndices, trainFraction=auxiliaryfunctions.LoadMetadata(os.path.join(cfg["project_path"],metadatafn))

            try:
                dlc_cfg = load_config(str(path_test_config))
            except FileNotFoundError:
                raise FileNotFoundError("It seems the model for shuffle %s and trainFraction %s does not exist."%(shuffle,trainFraction))

            #change batch size, if it was edited during analysis!
            dlc_cfg['batch_size']=1 #in case this was edited for analysis.

            #Create folder structure to store results.
            evaluationfolder=os.path.join(cfg["project_path"],str(auxiliaryfunctions.GetEvaluationFolder(trainFraction,shuffle,cfg)))
            auxiliaryfunctions.attempttomakefolder(evaluationfolder,recursive=True)
            #path_train_config = modelfolder / 'train' / 'pose_cfg.yaml'

            # Check which snapshots are available and sort them by # iterations
            Snapshots = np.array([fn.split('.')[0]for fn in os.listdir(os.path.join(str(modelfolder), 'train'))if "index" in fn])
            try: #check if any where found?
              Snapshots[0]
            except IndexError:

#Data=pd.read_hdf(os.path.join(cfg["project_path"],str(trainingsetfolder),'CollectedData_' + cfg["scorer"] + '.h5'),'df_with_missing')

    # Get list of body parts to evaluate network for
    comparisonbodyparts=auxiliaryfunctions.IntersectionofBodyPartsandOnesGivenbyUser(cfg,comparisonbodyparts)
    ##################################################
    # Load data...
    ##################################################
    trainFraction=cfg["TrainingFraction"][trainingsetindex]
    datafn,metadatafn=auxiliaryfunctions.GetDataandMetaDataFilenames(trainingsetfolder,trainFraction,shuffle,cfg)
    modelfolder=os.path.join(cfg["project_path"],str(auxiliaryfunctions.GetModelFolder(trainFraction,shuffle,cfg)))
    path_test_config = Path(modelfolder) / 'test' / 'pose_cfg.yaml'
    # Load meta data
    data, trainIndices, testIndices, trainFraction=auxiliaryfunctions.LoadMetadata(os.path.join(cfg["project_path"],metadatafn))

    try:
        dlc_cfg = load_config(str(path_test_config))
    except FileNotFoundError:
        raise FileNotFoundError("It seems the model for shuffle %s and trainFraction %s does not exist."%(shuffle,trainFraction))

    ########################### RESCALING (to global scale)
    if rescale==True:
        scale=dlc_cfg['global_scale']
        print("Rescaling Data to ", scale)
        Data=pd.read_hdf(os.path.join(cfg["project_path"],str(trainingsetfolder),'CollectedData_' + cfg["scorer"] + '.h5'),'df_with_missing')*scale
    else:
        scale=1
        Data=pd.read_hdf(os.path.join(cfg["project_path"],str(trainingsetfolder),'CollectedData_' + cfg["scorer"] + '.h5'),'df_with_missing')

    evaluationfolder=os.path.join(cfg["project_path"],str(auxiliaryfunctions.GetEvaluationFolder(trainFraction,shuffle,cfg)))
    # Check which snapshots are available and sort them by # iterations
    Snapshots = np.array([fn.split('.')[0]for fn in os.listdir(os.path.join(str(modelfolder), 'train'))if "index" in fn])

tf.reset_default_graph()
    start_path=os.getcwd() #record cwd to return to this directory in the end

    cfg = auxiliaryfunctions.read_config(config)
    trainFraction = cfg['TrainingFraction'][trainingsetindex]

    if cropping is not None:
        cfg['cropping']=True
        cfg['x1'],cfg['x2'],cfg['y1'],cfg['y2']=cropping
        print("Overwriting cropping parameters:", cropping)
        print("These are used for all videos, but won't be save to the cfg file.")

    modelfolder=os.path.join(cfg["project_path"],str(auxiliaryfunctions.GetModelFolder(trainFraction,shuffle,cfg)))
    path_test_config = Path(modelfolder) / 'test' / 'pose_cfg.yaml'
    try:
        dlc_cfg = load_config(str(path_test_config))
    except FileNotFoundError:
        raise FileNotFoundError("It seems the model for shuffle %s and trainFraction %s does not exist."%(shuffle,trainFraction))

    # Check which snapshots are available and sort them by # iterations
    try:
      Snapshots = np.array([fn.split('.')[0]for fn in os.listdir(os.path.join(modelfolder , 'train'))if "index" in fn])
    except FileNotFoundError:
      raise FileNotFoundError("Snapshots not found! It seems the dataset for shuffle %s has not been trained/does not exist.\n Please train it before using it to analyze videos.\n Use the function 'train_network' to train the network for shuffle %s."%(shuffle,shuffle))

    if cfg['snapshotindex'] == 'all':
        print("Snapshotindex is set to 'all' in the config.yaml file. Running video analysis with all snapshots is very costly! Use the function 'evaluate_network' to choose the best the snapshot. For now, changing snapshot index to -1!")
        snapshotindex = -1
    else:
        snapshotindex=cfg['snapshotindex']

    increasing_indices = np.argsort([int(m.split('-')[1]) for m in Snapshots])

vers = (tf.__version__).split('.')
    if int(vers[0]) == 1 and int(vers[1]) > 12:
        TF = tf.compat.v1
    else:
        TF = tf

    TF.reset_default_graph()
    start_path=os.getcwd() #record cwd to return to this directory in the end

    cfg = auxiliaryfunctions.read_config(config)
    trainFraction = cfg['TrainingFraction'][trainingsetindex]
    modelfolder=os.path.join(cfg["project_path"],str(auxiliaryfunctions.GetModelFolder(trainFraction,shuffle,cfg)))
    path_test_config = Path(modelfolder) / 'test' / 'pose_cfg.yaml'
    try:
        dlc_cfg = load_config(str(path_test_config))
    except FileNotFoundError:
        raise FileNotFoundError("It seems the model for shuffle %s and trainFraction %s does not exist."%(shuffle,trainFraction))
    # Check which snapshots are available and sort them by # iterations
    try:
      Snapshots = np.array([fn.split('.')[0]for fn in os.listdir(os.path.join(modelfolder , 'train'))if "index" in fn])
    except FileNotFoundError:
      raise FileNotFoundError("Snapshots not found! It seems the dataset for shuffle %s has not been trained/does not exist.\n Please train it before using it to analyze videos.\n Use the function 'train_network' to train the network for shuffle %s."%(shuffle,shuffle))

    if cfg['snapshotindex'] == 'all':
        print("Snapshotindex is set to 'all' in the config.yaml file. Running video analysis with all snapshots is very costly! Use the function 'evaluate_network' to choose the best the snapshot. For now, changing snapshot index to -1!")
        snapshotindex = -1
    else:
        snapshotindex=cfg['snapshotindex']

    increasing_indices = np.argsort([int(m.split('-')[1]) for m in Snapshots])
    Snapshots = Snapshots[increasing_indices]

def __init__(self, cfg_path):

        self.cfg_path = cfg_path
        self.cfg = edict(load_config(cfg_path))
        self.cfg.batch_size = 1

        self.sess, self.inputs, self.outputs = self.setup_frozen_prediction()

def display_dataset():
    logging.basicConfig(level=logging.DEBUG)

    cfg = load_config()
    dataset = dataset_create(cfg)
    dataset.set_shuffle(False)

    while True:
        batch = dataset.next_batch()

        for frame_id in range(1):
            img = batch[Batch.inputs][frame_id,:,:,:]
            img = np.squeeze(img).astype('uint8')

            scmap = batch[Batch.part_score_targets][frame_id,:,:,:]
            scmap = np.squeeze(scmap)

            # scmask = batch[Batch.part_score_weights]
            # if scmask.size > 1:
            #     scmask = np.squeeze(scmask).astype('uint8')

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)

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)