How to use the pathlib.Path function in pathlib

def _check_line_number(self,
                           line_number: int,
                           tail: str,
                           expected_line_part: str,
                           num_line_number_previously_requested: int = 0
                           ):
        root_dir = pathlib.Path('root')
        logger = sut.PhaseLoggingPaths(root_dir, 'phase-identifier')
        for i in range(num_line_number_previously_requested):
            logger.for_line(line_number)
        actual = logger.for_line(line_number, tail=tail)
        self.assertEqual(logger.dir_path / expected_line_part,
                         actual)

def test_get_path(remotefile):
    get(remotefile, 'testget')
    with Path('testget').open() as f:
        assert f.read() == 'foobarééœ'
    Path('testget').unlink()

parser.add_argument("--adam_epsilon", default=1e-8, type=float,
                        help="Epsilon for Adam optimizer.")
    parser.add_argument('--max_grad_norm', default=1.0, type=float)
    parser.add_argument("--learning_rate", default=0.00176, type=float,
                        help="The initial learning rate for Adam.")
    parser.add_argument('--seed', type=int, default=42,
                        help="random seed for initialization")
    parser.add_argument('--fp16_opt_level', type=str, default='O2',
                        help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
                             "See details at https://nvidia.github.io/apex/amp.html")
    parser.add_argument('--fp16', action='store_true',
                        help="Whether to use 16-bit float precision instead of 32-bit")
    args = parser.parse_args()

    args.data_dir = Path(args.data_dir)
    args.output_dir = Path(args.output_dir)

    pregenerated_data = args.data_dir / "corpus/train"
    init_logger(log_file=str(args.output_dir/ "train_albert_model.log"))
    assert pregenerated_data.is_dir(), \
        "--pregenerated_data should point to the folder of files made by prepare_lm_data_mask.py!"

    samples_per_epoch = 0
    for i in range(args.file_num):
        data_file = pregenerated_data / f"{args.data_name}_file_{i}.json"
        metrics_file = pregenerated_data / f"{args.data_name}_file_{i}_metrics.json"
        if data_file.is_file() and metrics_file.is_file():
            metrics = json.loads(metrics_file.read_text())
            samples_per_epoch += metrics['num_training_examples']
        else:
            if i == 0:
                exit("No training data was found!")

def serve_store_model(model_name):
    with open(Path(config.DATASTORE_DIR) / f'models/{model_name}.pkl', 'rb') as f:
        r.set(f'model:{model_name}', f.read())

    return f'Stored model {model_name}'

def dummy_iter(ids, images, cameras):
    """ Standard loaders (shared across the evaluation pipelines) require at
        least a dictionary with an item name and an image whose shape can be
        needed. To avoid reading images from disk unnecessarily, we create
        dummy images that have a shape but no data.
    """
    for i in ids:
        im = images[i]
        cam = cameras[im.camera_id]
        name = im.name
        yield {'name': Path(Path(name).parent, Path(name).stem).as_posix(),
               'image': DummyImage((cam.height, cam.width, 1))}

def _get_translations_path() -> str:
    path = Path(__file__)
    path = path.parent.joinpath("resources", "locale")
    if path.is_dir():
        return str(path)

    return sys.prefix + "/share/locale"

rows.append("Report.EventNaturalInterval.ConditionalAllIntervalStandardDeviation=false")
            rows.append("")
            rows.append("AllCodesMutuallyExclusive=true")
            rows.append("")

            for (behav, key) in all_observed_behaviors:
                rows.append(f"Behavior.isModified.{key}=false")
                rows.append(f"Behavior.isSubtracted.{key}=false")
                rows.append(f"Behavior.isIgnored.{key}=false")
                rows.append(f"Behavior.isEventAnalyzed.{key}=false")
                rows.append(f"Behavior.switchesOff.{key}=")
                rows.append("")

            if faf_creation_answer == "" or faf_creation_answer == OVERWRITE:
                try:
                    with open(pathlib.Path(file_name_subject).with_suffix(".faf"), "w") as f_out:
                        f_out.write("\n".join(rows))
                except Exception:
                    return False, f"File FAF not created: {sys.exc_info()[1]}"

        return True, ""

    except Exception:
        logging.critical("Error during exporting the events for JWatcher")
        dialog.error_message("exporting the events for JWatcher", sys.exc_info())
        return False, ""

def get_artwork_path(self):
        root = pathlib.Path(__file__).parents[2]
        artwork_path = root.joinpath("artwork")
        if not artwork_path.is_dir():
            raise FileNotFoundError
        return artwork_path

import pathlib

from pydo import *

this_dir = pathlib.Path(__file__).parent

package = {
    'requires': ['qt'],
    'sysroot_debs': [],
    'root_debs': [],
    'target': this_dir / 'qmlrss.tar.gz',
    'install': ['{chroot} {stage} /bin/systemctl reenable qmlrss.service'],
}

from .. import qt
from ... import jobs

builddir = this_dir / 'build'
stage = this_dir / 'stage'
repo = this_dir / 'qmlrss'

def init_persistence(self, out_dir_override=None):
		"""
		Storage locations for results and intermediate data
		"""

		out_dir_default = Path('{channel.ctx.workdir}') / '{dset.name}_{dset.split}' / f'sem_{self.sem_seg_variant}'
		out_dir = Path(out_dir_override or out_dir_default)

		self.persistence_base_dir = out_dir

		# outputs of the pipelines steps: 
		self.storage = dict(
			# semantic segmentation
			pred_labels_trainIds = ChannelLoaderImage(out_dir / 'sem_labels/{fid}_predTrainIds.png'),
			pred_labels_colorimg = ChannelLoaderImage(out_dir / 'sem_labels/{fid}_predColorImg.png'),

			# synthesized image
			gen_image = ChannelLoaderImage(out_dir / 'gen_image/{fid}_gen_image.webp'),

			# overview
			demo_with_labels = ChannelLoaderImage(out_dir / 'demo/{fid_no_slash}_pipeline.webp'),
			demo_with_baselines = ChannelLoaderImage(out_dir / 'demo/{fid_no_slash}_scores.webp'),
		)