How to use the cornac.utils.cache function in cornac

def load_trust(reader: Reader = None) -> List:
    """Load the user trust information (undirected network)

    Parameters
    ----------
    reader: `obj:cornac.data.Reader`, default: None
        Reader object used to read the data.

    Returns
    -------
    data: array-like
        Data in the form of a list of tuples (source_user, target_item, trust_value).

    """
    fpath = cache(url='http://www.trustlet.org/datasets/downloaded_epinions/trust_data.txt.bz2',
                  unzip=True, relative_path='trust_data.txt', cache_dir=_get_cache_dir())
    reader = Reader() if reader is None else reader
    return reader.read(fpath, sep=' ')

reader: `obj:cornac.data.Reader`, optional, default: None
        Reader object used to read the data.

    Returns
    -------
    data: array-like
        Data in the form of a list of tuples depending on the given data format.
    """

    fmt = validate_format(fmt, VALID_DATA_FORMATS)

    if variant not in VARIANTS:
        raise ValueError('variant must be one of {}.'.format(VARIANTS))

    fpath = cache(url=URL[variant], unzip=UNZIP[variant], relative_path=RELATIVE_PATH[variant])
    reader = Reader() if reader is None else reader
    return reader.read(fpath, fmt, sep=SEP[variant])

def load_feedback(reader: Reader = None) -> List:
    """Load the implicit feedback between users and items

    Parameters
    ----------
    reader: `obj:cornac.data.Reader`, default: None
        Reader object used to read the data.

    Returns
    -------
    data: array-like
        Data in the form of a list of tuples (user, item, 1).

    """
    fpath = cache(url='https://static.preferred.ai/cornac/datasets/citeulike/users.zip',
                  relative_path='citeulike/users.dat', unzip=True)
    reader = Reader() if reader is None else reader
    return reader.read(fpath, fmt='UI', sep=' ', id_inline=True)

def load_feedback(reader: Reader = None) -> List:
    """Load the user-item ratings, scale: [1,5]

    Parameters
    ----------
    reader: `obj:cornac.data.Reader`, default: None
        Reader object used to read the data.

    Returns
    -------
    data: array-like
        Data in the form of a list of tuples (user, item, rating).
    """
    fpath = cache(url='https://static.preferred.ai/cornac/datasets/amazon_clothing/rating.zip',
                  unzip=True, relative_path='amazon_clothing/rating.txt')
    reader = Reader() if reader is None else reader
    return reader.read(fpath, sep='\t')

Data file name.

    fmt: str, default: 'UIR'
        Data format to be returned.

    reader: `obj:cornac.data.Reader`, default: None
        Reader object used to read the data.

    Returns
    -------
    data: array-like
        Data in the form of a list of tuples depending on the given data format.

    """
    fmt = validate_format(fmt, VALID_DATA_FORMATS)
    fpath = cache(url='https://static.preferred.ai/cornac/datasets/netflix/{}.zip'.format(fname),
                  unzip=True, relative_path='netflix/{}.csv'.format(fname))
    reader = Reader() if reader is None else reader
    return reader.read(fpath, fmt, sep=',')

def load_text():
    """Load item texts including tile and abstract joined together into one document per item.

    Returns
    -------
    texts: List
        List of text documents, one per item.

    ids: List
        List of item ids aligned with indices in `texts`.
    """
    import csv

    texts, ids = [], []
    fpath = cache(url='https://static.preferred.ai/cornac/datasets/citeulike/text.zip',
                  relative_path='citeulike/raw-data.csv', unzip=True)
    with open(fpath, 'r', encoding='utf-8', errors='ignore') as f:
        next(f)
        for row in csv.reader(f, delimiter=',', quotechar='"'):
            ids.append(row[0])
            texts.append(row[3] + '. ' + row[4])

    return texts, ids

Parameters
    ----------
    reader: `obj:cornac.data.Reader`, default: None
        Reader object used to read the data.

    Returns
    -------
    data: array-like
        Data in the form of a list of tuples (user, item, [(aspect, opinion, sentiment), (aspect, opinion, sentiment), ...]).

    References
    ----------
    Gao, J., Wang, X., Wang, Y., & Xie, X. (2019). Explainable Recommendation Through Attentive Multi-View Learning. AAAI.
    """
    fpath = cache(url='https://static.preferred.ai/cornac/datasets/amazon_toy/sentiment.zip',
                  unzip=True, relative_path='amazon_toy/sentiment.txt')
    reader = Reader() if reader is None else reader
    return reader.read(fpath, fmt='UITup', sep=',', tup_sep=':')

def load_feature():
    """Load the item visual feature

    Returns
    -------
    features: numpy.ndarray
        Feature matrix with shape (n, 4096) with n is the number of items.

    item_ids: List
        List of item ids aligned with indices in `features`.
    """
    features = np.load(cache(url='https://static.preferred.ai/cornac/datasets/tradesy/item_features.zip',
                             unzip=True, relative_path='tradesy/item_features.npy'))
    item_ids = read_text(cache(url='https://static.preferred.ai/cornac/datasets/tradesy/item_ids.zip',
                               unzip=True, relative_path='tradesy/item_ids.txt'))
    return features, item_ids

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Example to train and evaluate a model with given data"""

from cornac.data import Reader
from cornac.eval_methods import BaseMethod
from cornac.models import MF
from cornac.metrics import MAE, RMSE
from cornac.utils import cache

# Download MovieLens 100K provided training and test splits
reader = Reader()
train_data = reader.read(cache(url='http://files.grouplens.org/datasets/movielens/ml-100k/u1.base'))
test_data = reader.read(cache(url='http://files.grouplens.org/datasets/movielens/ml-100k/u1.test'))

eval_method = BaseMethod.from_splits(train_data=train_data, test_data=test_data,
                                     exclude_unknowns=False, verbose=True)

mf = MF(k=10, max_iter=25, learning_rate=0.01, lambda_reg=0.02,
        use_bias=True, early_stop=True, verbose=True)

# Evaluation
result = eval_method.evaluate(model=mf, metrics=[MAE(), RMSE()], user_based=True)
print(result)

def load_graph(reader: Reader = None) -> List:
    """Load the item-item interactions (symmetric network), built from the Amazon Also-Viewed information

    Parameters
    ----------
    reader: `obj:cornac.data.Reader`, default: None
        Reader object used to read the data.

    Returns
    -------
    data: array-like
        Data in the form of a list of tuples (item, item, 1).
    """
    fpath = cache(url='https://static.preferred.ai/cornac/datasets/amazon_office/context.zip',
                  unzip=True, relative_path='amazon_office/context.txt')
    reader = Reader() if reader is None else reader
    return reader.read(fpath, sep=' ')