How to use the deeplabcut.utils.auxiliaryfunctions.write_config function in deeplabcut

cfg = auxiliaryfunctions.read_config(config)
    project_path = str(Path(config).parents[0])

    cfg['project_path'] = project_path
    if 'Reaching' in project_path:
        video_file = os.path.join(project_path, 'videos','reachingvideo1.avi')
    elif 'openfield' in project_path:
        video_file = os.path.join(project_path, 'videos','m4s1.mp4')
    else:
        print("This is not an offical demo dataset.")
    
    if 'WILL BE AUTOMATICALLY UPDATED BY DEMO CODE' in cfg['video_sets'].keys():
        cfg['video_sets'][str(video_file)] = cfg['video_sets'].pop('WILL BE AUTOMATICALLY UPDATED BY DEMO CODE')

    auxiliaryfunctions.write_config(config,cfg)

Linux/MacOs
    >>> deeplabcut.create_pretrained_human_project('human','Linus',['/data/videos/mouse1.avi'],'/analysis/project/',copy_videos=False)

    Windows:
    >>> deeplabcut.create_pretrained_human_project('human','Bill',[r'C:\yourusername\rig-95\Videos\reachingvideo1.avi'],r'C:\yourusername\analysis\project' copy_videos=False)
    Users must format paths with either:  r'C:\ OR 'C:\\ <- i.e. a double backslash \ \ )
    --------
    """

    cfg=deeplabcut.create_new_project(project,experimenter,videos,working_directory,copy_videos,videotype)

    config = auxiliaryfunctions.read_config(cfg)
    config['bodyparts'] = ['ankle1','knee1','hip1','hip2','knee2','ankle2','wrist1','elbow1','shoulder1','shoulder2','elbow2','wrist2','chin','forehead']
    config['skeleton'] = [['ankle1', 'knee1'],['ankle2', 'knee2'],['knee1', 'hip1'],['knee2', 'hip2'],['hip1', 'hip2'], ['shoulder1', 'shoulder2'], ['shoulder1', 'hip1'], ['shoulder2', 'hip2'], ['shoulder1', 'elbow1'], ['shoulder2', 'elbow2'], ['chin', 'forehead'], ['elbow1', 'wrist1'], ['elbow2', 'wrist2']]
    config['default_net_type']='resnet_101'
    auxiliaryfunctions.write_config(cfg,config)
    config = auxiliaryfunctions.read_config(cfg)

    train_dir = Path(os.path.join(config['project_path'],str(auxiliaryfunctions.GetModelFolder(trainFraction=config['TrainingFraction'][0],shuffle=1,cfg=config)),'train'))
    test_dir = Path(os.path.join(config['project_path'],str(auxiliaryfunctions.GetModelFolder(trainFraction=config['TrainingFraction'][0],shuffle=1,cfg=config)),'test'))

    # Create the model directory
    train_dir.mkdir(parents=True,exist_ok=True)
    test_dir.mkdir(parents=True,exist_ok=True)

    modelfoldername=auxiliaryfunctions.GetModelFolder(trainFraction=config['TrainingFraction'][0],shuffle=1,cfg=config)

    path_train_config = str(os.path.join(config['project_path'],Path(modelfoldername),'train','pose_cfg.yaml'))
    path_test_config = str(os.path.join(config['project_path'],Path(modelfoldername),'test','pose_cfg.yaml'))

    # Download the weights and put then in appropriate directory
    cwd = os.getcwd()

elif os.path.isfile(os.path.join(folder,'CollectedData_'+cfg['scorer']+'.h5')): #Folder that contains human data set...
            pass
        else:
            print("The following folder was not manually refined,...",folder)
            flagged=True
            pass #this folder does not contain a MachineLabelsRefine file (not updated...)

    if flagged==False:
        # updates iteration by 1
        iter_prev=cfg['iteration']
        if not forceiterate:
            cfg['iteration']=int(iter_prev+1)
        else:
            cfg['iteration']=forceiterate

        auxiliaryfunctions.write_config(config,cfg)

        print("Merged data sets and updated refinement iteration to "+str(cfg['iteration'])+".")
        print("Now you can create a new training set for the expanded annotated images (use create_training_dataset).")
    else:
        print("Please label, or remove the un-corrected folders.")

cfg_file['x2']=640
    cfg_file['y1']=277
    cfg_file['y2']=624
    cfg_file['batch_size']=8 #batch size during inference (video - analysis); see https://www.biorxiv.org/content/early/2018/10/30/457242
    cfg_file['corner2move2']=(50,50)
    cfg_file['move2corner']=True
    cfg_file['skeleton']=[['bodypart1','bodypart2'],['objectA','bodypart3']]
    cfg_file['skeleton_color']='black'
    cfg_file['pcutoff']=0.1
    cfg_file['dotsize']=12 #for plots size of dots
    cfg_file['alphavalue']=0.7 #for plots transparency of markers
    cfg_file['colormap']='jet' #for plots type of colormap

    projconfigfile=os.path.join(str(project_path),'config.yaml')
    # Write dictionary to yaml  config file
    auxiliaryfunctions.write_config(projconfigfile,cfg_file)

    print('Generated "{}"'.format(project_path / 'config.yaml'))
    print("\nA new project with name %s is created at %s and a configurable file (config.yaml) is stored there. Change the parameters in this file to adapt to your project's needs.\n Once you have changed the configuration file, use the function 'extract_frames' to select frames for labeling.\n. [OPTIONAL] Use the function 'add_new_videos' to add new videos to your project (at any stage)." %(project_name,str(wd)))
    return projconfigfile

for vid in toberemoved:
        del cfg['video_sets'][vid]

    #Load updated lists:
    videos = cfg['video_sets'].keys()
    video_names = [Path(i).stem for i in videos]

    for vn in alldatafolders:
        if vn in video_names:
            pass
        else:
            print(vn, " is missing in config file >> adding it!")
            #cfg['video_sets'][vn]
            cfg['video_sets'].update({os.path.join(prefix,vn+suffix) : {'crop': ', '.join(map(str, [0, width, 0, height]))}})

    auxiliaryfunctions.write_config(config,cfg)

video_path = os.readlink(video)

        vcap = cv2.VideoCapture(video_path)
        if vcap.isOpened():
            # get vcap property
           width = int(vcap.get(cv2.CAP_PROP_FRAME_WIDTH))
           height = int(vcap.get(cv2.CAP_PROP_FRAME_HEIGHT))
           if coords == None:
                cfg['video_sets'].update({video_path : {'crop': ', '.join(map(str, [0, width, 0, height]))}})
           else:
                c = coords[idx]
                cfg['video_sets'].update({video_path : {'crop': ', '.join(map(str, c))}})
        else:
           print("Cannot open the video file!")

    auxiliaryfunctions.write_config(config,cfg)
    print("New video was added to the project! Use the function 'extract_frames' to select frames for labeling.")

self.end_frames_sizer.ShowItems(show=True)
        self.widget_panel.Layout()
        self.slider.SetMax(self.numberFrames)
        self.startFrame.SetMax(self.numberFrames-1)
        self.endFrame.SetMax(self.numberFrames)
        self.x1 = int(self.new_x1)
        self.x2 = int(self.new_x2)
        self.y1 = int(self.new_y1)
        self.y2 = int(self.new_y2)
        self.canvas.mpl_disconnect(self.cid)
        self.axes.clear()
        self.currFrame = (self.slider.GetValue())
        self.update()
# Update the config.yaml file
        self.cfg['video_sets'][self.video_source] = {'crop': ', '.join(map(str, [self.x1, self.x2, self.y1, self.y2]))}
        auxiliaryfunctions.write_config(self.config_path,self.cfg)