How to use the continuum.continuum_loader.ContinuumSetLoader function in continuum

def test_DataLoader_init(get_fake_dataset):
    fake_dataset = get_fake_dataset
    dataset = ContinuumSetLoader(fake_dataset)

    if not dataset.current_task == 0:
        raise AssertionError("Test fail")

def test_DataLoader_init_label_size(get_fake_dataset):
    """
    Test if the dictionnary of label have the good size
    :param get_fake_dataset:
    :return:
    """
    fake_dataset = get_fake_dataset
    dataset = ContinuumSetLoader(fake_dataset)

    if not len(dataset.labels) == dataset_size:
        raise AssertionError("Test fail")

def test_DataLoader_with_torch(get_fake_dataset):
    """
    Test if the dataloader can be used with torch.utils.data.DataLoader
    :param get_fake_dataset:
    :return:
    """
    fake_dataset = get_fake_dataset
    dataset = ContinuumSetLoader(fake_dataset)
    train_loader = data.DataLoader(dataset, batch_size=10, shuffle=True, num_workers=6)

    for _, (_, _) in enumerate(train_loader):
        break

def test_DataLoader_with_torch_loader(get_fake_dataset):
    """
    Test if the dataloader with torch.utils.data.DataLoader provide data of good type
    :param get_fake_dataset:
    :return:
    """
    fake_dataset = get_fake_dataset
    dataset = ContinuumSetLoader(fake_dataset)
    train_loader = data.DataLoader(dataset, batch_size=10, shuffle=True, num_workers=6)

    for _, (batch, label) in enumerate(train_loader):

        if not isinstance(label, torch.LongTensor):
            raise AssertionError("Test fail")

        if not isinstance(batch, torch.FloatTensor):
            raise AssertionError("Test fail")
        break

def test_DataLoader_init_label_is_dict(get_fake_dataset):
    """
    Test if the dictionnary of label is really a dictionnary
    :param get_fake_dataset:
    :return:
    """
    fake_dataset = get_fake_dataset
    dataset = ContinuumSetLoader(fake_dataset)

    if not isinstance(dataset.labels, dict):
        raise AssertionError("Test fail")

import os.path
import torch
from copy import deepcopy
from .continuum_loader import ContinuumSetLoader
from .data_utils import load_data, check_and_Download_data, get_images_format


class ContinuumBuilder(ContinuumSetLoader):
    '''Parent Class for Sequence Formers'''

    def __init__(self, path, dataset, tasks_number, scenario, num_classes, download=False, train=True, path_only=False, verbose=False):

        self.tasks_number = tasks_number
        self.num_classes = num_classes
        self.dataset = dataset
        self.i = os.path.join(path, "Datasets")
        self.o = os.path.join(path, "Continua", self.dataset)
        self.train = train
        self.imageSize, self.img_channels = get_images_format(self.dataset)
        self.scenario = scenario
        self.verbose = verbose
        self.path_only = path_only
        self.download = download