How to use the deeplabcut.create_new_project function in deeplabcut

# For testing a color video:
#videoname='baby4hin2min'
#video=[os.path.join('/home/alex/Desktop/Data',videoname+'.mp4')]
#to test destination folder:
#dfolder=basepath

dfolder=None
net_type='resnet_50' #'mobilenet_v2_0.35' #'resnet_50'
augmenter_type='default' #'tensorpack'
augmenter_type2='imgaug'
augmenter_type3='tensorpack'
numiter=5

print("CREATING PROJECT")
path_config_file=deeplabcut.create_new_project(task,scorer,video,copy_videos=True)

cfg=deeplabcut.auxiliaryfunctions.read_config(path_config_file)
cfg['numframes2pick']=5
cfg['pcutoff']=0.01
cfg['TrainingFraction']=[.8]
cfg['skeleton']=[['bodypart1','bodypart2'],['bodypart1','bodypart3']]

deeplabcut.auxiliaryfunctions.write_config(path_config_file,cfg)

print("EXTRACTING FRAMES")
deeplabcut.extract_frames(path_config_file,mode='automatic',userfeedback=False)

print("CREATING-SOME LABELS FOR THE FRAMES")
frames=os.listdir(os.path.join(cfg['project_path'],'labeled-data',videoname))
#As this next step is manual, we update the labels by putting them on the diagonal (fixed for all frames)
for index,bodypart in enumerate(cfg['bodyparts']):

import os,  subprocess, deeplabcut
from pathlib import Path
import pandas as pd
import numpy as np

print("Imported DLC!")
basepath=os.path.dirname(os.path.abspath('testscript.py'))
videoname='reachingvideo1'
video=[os.path.join(basepath,'Reaching-Mackenzie-2018-08-30','videos',videoname+'.avi')]

#to test destination folder:
#dfolder=basepath
dfolder=None

print("CREATING PROJECT")
path_config_file=deeplabcut.create_new_project(task,scorer,video,copy_videos=True)

cfg=deeplabcut.auxiliaryfunctions.read_config(path_config_file)
cfg['numframes2pick']=5
cfg['pcutoff']=0.01
cfg['TrainingFraction']=[.8]
cfg['default_net_type']='resnet_152'

deeplabcut.auxiliaryfunctions.write_config(path_config_file,cfg)

print("EXTRACTING FRAMES")
deeplabcut.extract_frames(path_config_file,mode='automatic',userfeedback=False)

print("CREATING-SOME LABELS FOR THE FRAMES")
frames=os.listdir(os.path.join(cfg['project_path'],'labeled-data',videoname))
#As this next step is manual, we update the labels by putting them on the diagonal (fixed for all frames)
for index,bodypart in enumerate(cfg['bodyparts']):

path = Path(configname)
    cfg = ruamelFile.load(path)
    return(cfg)

def write_config(configname,cfg):
    with open(configname, 'w') as cf:
        ruamelFile = ruamel.yaml.YAML()
        ruamelFile.dump(cfg, cf)

print("Imported DLC!")
basepath=os.path.dirname(os.path.abspath('testscript.py'))
videoname='reachingvideo1'
video=[os.path.join(basepath,'Reaching-Mackenzie-2018-08-30/videos/'+videoname+'.avi')]

print("CREATING PROJECT")
path_config_file=deeplabcut.create_new_project(task,scorer,video,copy_videos=True)
cfg=read_config(path_config_file)
cfg['numframes2pick']=5
cfg['pcutoff']=0.01
cfg['TrainingFraction']=[.8]

write_config(path_config_file,cfg)

print("EXTRACTING FRAMES")
deeplabcut.extract_frames(path_config_file,mode='automatic',algo='kmeans')

print("CREATING-SOME LABELS FOR THE FRAMES")
frames=os.listdir(os.path.join(cfg['project_path'],'labeled-data',videoname))
#As this next step is manual, we update the labels by putting them on the diagonal (fixed for all frames)
for index,bodypart in enumerate(cfg['bodyparts']):
        columnindex = pd.MultiIndex.from_product([[scorer], [bodypart], ['x', 'y']],names=['scorer', 'bodyparts', 'coords'])
        frame = pd.DataFrame(np.ones((len(frames),2))*50*index, columns = columnindex, index = [os.path.join('labeled-data',videoname,fn) for fn in frames])

Finally create the new project
        """
        if self.sel_config.IsShown():
            self.cfg = self.sel_config.GetPath()
            if self.cfg == "":
                wx.MessageBox('Please choose the config.yaml file to load the project', 'Error', wx.OK | wx.ICON_ERROR)
                self.loaded = False
            else:
                wx.MessageBox('Project Loaded!', 'Info', wx.OK | wx.ICON_INFORMATION)
                self.loaded = True
                self.edit_config_file.Enable(True)
        else:
            self.task = self.proj_name_txt_box.GetValue()
            self.scorer = self.exp_txt_box.GetValue()
            if self.task!="" and self.scorer!="" and self.filelist!=[]:
                self.cfg=deeplabcut.create_new_project(self.task,self.scorer,self.filelist,self.dir,self.copy)
            else:
                wx.MessageBox('Some of the enteries are missing.\n\nMake sure that the task and experimenter name are specified and videos are selected!', 'Error', wx.OK | wx.ICON_ERROR)
                self.cfg = False
            if self.cfg:
                wx.MessageBox('New Project Created', 'Info', wx.OK | wx.ICON_INFORMATION)
                self.loaded = True
                self.edit_config_file.Enable(True)

        # Remove the pages in case the user goes back to the create new project and creates/load a new project
        if self.parent.GetPageCount() > 3:
            for i in range(2,self.parent.GetPageCount()):
                self.parent.RemovePage(2)
                self.parent.Layout()

        # Add all the other pages
        if self.loaded:

``True`` or ``False``.
    analyzevideo " bool, optional
        If true, then the video is analzyed and a labeled video is created. If false, then only the project will be created and the weights downloaded. You can then access them

    Example
    --------
    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)