How to use the autogluon.utils.tabular.utils.s3_utils.is_s3_url function in autogluon

if isinstance(path, list):
        return load_multipart(
            paths=path, delimiter=delimiter, encoding=encoding, columns_to_keep=columns_to_keep,
            dtype=dtype, error_bad_lines=error_bad_lines, header=header, names=names, format=format,
            nrows=nrows, skiprows=skiprows, usecols=usecols, low_memory=low_memory, converters=converters,
            filters=filters,
            worker_count=worker_count,
            multiprocessing_method=multiprocessing_method
        )
    if format is not None:
        pass
    elif path.endswith(save_pointer.POINTER_SUFFIX):
        format = 'pointer'
    elif path[-1] == '/' and s3_utils.is_s3_url(path):  # and path[:2] == 's3'
        format = 'multipart_s3'
    elif path[-1] == '/' and not s3_utils.is_s3_url(path):  # and path[:2] != 's3'
        format = 'multipart_local'
    elif '.parquet' in path or path[-1] == '/':
        format = 'parquet'
    else:
        format = 'csv'

    if format == 'pointer':
        content_path = load_pointer.get_pointer_content(path)
        return load(path=content_path, delimiter=delimiter, encoding=encoding, columns_to_keep=columns_to_keep, dtype=dtype, 
                    error_bad_lines=error_bad_lines, header=header, names=names, format=None, nrows=nrows, skiprows=skiprows, 
                    usecols=usecols, low_memory=low_memory, converters=converters, filters=filters, sample_count=sample_count, 
                    worker_count=worker_count, multiprocessing_method=multiprocessing_method)
    elif format == 'multipart_s3':
        bucket, prefix = s3_utils.s3_path_to_bucket_prefix(path)
        return load_multipart_s3(bucket=bucket, prefix=prefix, columns_to_keep=columns_to_keep, dtype=dtype, filters=filters, 
                                 sample_count=sample_count, worker_count=worker_count, multiprocessing_method=multiprocessing_method)  # TODO: Add arguments!

def load_with_fn(path, pickle_fn, format=None, verbose=True):
    if path.endswith('.pointer'):
        format = 'pointer'
    elif s3_utils.is_s3_url(path):
        format = 's3'
    if format == 'pointer':
        content_path = load_pointer.get_pointer_content(path)
        if content_path == path:
            raise RecursionError('content_path == path! : ' + str(path))
        return load_with_fn(content_path, pickle_fn)
    elif format == 's3':
        if verbose: logger.log(15, 'Loading: %s' % path)
        s3_bucket, s3_prefix = s3_utils.s3_path_to_bucket_prefix(s3_path=path)
        s3 = boto3.resource('s3')
        # Has to be wrapped in IO buffer since s3 stream does not implement seek()
        buff = io.BytesIO(s3.Bucket(s3_bucket).Object(s3_prefix).get()['Body'].read())
        return pickle_fn(buff)

    if verbose: logger.log(15, 'Loading: %s' % path)
    with open(path, 'rb') as fin:

names=None, format=None, nrows=None, skiprows=None, usecols=None, low_memory=False, converters=None, 
         filters=None, sample_count=None, worker_count=None, multiprocessing_method='forkserver') -> DataFrame:
    if isinstance(path, list):
        return load_multipart(
            paths=path, delimiter=delimiter, encoding=encoding, columns_to_keep=columns_to_keep,
            dtype=dtype, error_bad_lines=error_bad_lines, header=header, names=names, format=format,
            nrows=nrows, skiprows=skiprows, usecols=usecols, low_memory=low_memory, converters=converters,
            filters=filters,
            worker_count=worker_count,
            multiprocessing_method=multiprocessing_method
        )
    if format is not None:
        pass
    elif path.endswith(save_pointer.POINTER_SUFFIX):
        format = 'pointer'
    elif path[-1] == '/' and s3_utils.is_s3_url(path):  # and path[:2] == 's3'
        format = 'multipart_s3'
    elif path[-1] == '/' and not s3_utils.is_s3_url(path):  # and path[:2] != 's3'
        format = 'multipart_local'
    elif '.parquet' in path or path[-1] == '/':
        format = 'parquet'
    else:
        format = 'csv'

    if format == 'pointer':
        content_path = load_pointer.get_pointer_content(path)
        return load(path=content_path, delimiter=delimiter, encoding=encoding, columns_to_keep=columns_to_keep, dtype=dtype, 
                    error_bad_lines=error_bad_lines, header=header, names=names, format=None, nrows=nrows, skiprows=skiprows, 
                    usecols=usecols, low_memory=low_memory, converters=converters, filters=filters, sample_count=sample_count, 
                    worker_count=worker_count, multiprocessing_method=multiprocessing_method)
    elif format == 'multipart_s3':
        bucket, prefix = s3_utils.s3_path_to_bucket_prefix(path)

def save(path, df, index=False, verbose=True, type=None, sep=',', compression='gzip', header=True, json_dump_columns=None):
    if json_dump_columns is not None:
        df = df.copy()
        for column in json_dump_columns:
            if column in df.columns.values:
                df[column] = [json.dumps(x[0]) for x in zip(df[column])]
    if type is None:
        if path[-1] == '/' and s3_utils.is_s3_url(path):  # and path[:2] == 's3'
            type = 'multipart_s3'
        elif path[-1] == '/' and not s3_utils.is_s3_url(path):  # and path[:2] != 's3'
            type = 'multipart_local'
        elif '.csv' in path:
            type = 'csv'
        elif '.parquet' in path:
            type = 'parquet'
        else:
            type = 'csv'
    if 's3' not in path[:2]:
        is_local = True
    else:
        is_local = False
    column_count = len(list(df.columns.values))
    row_count = df.shape[0]
    if is_local:
        os.makedirs(os.path.dirname(path), exist_ok=True)
    if type == 'csv':