How to use the gcsfs.GCSFileSystem function in gcsfs

            ("gcs://bucket/file.json", GCSFileSystem),
        ],
    )
    def test_protocol_usage(self, filepath, instance_type):
        data_set = JSONDataSet(filepath=filepath)
        assert isinstance(data_set._fs, instance_type)
        assert str(data_set._filepath) == data_set._fs._strip_protocol(filepath)
        assert isinstance(data_set._filepath, PurePosixPath)

def __init__(self):
        self.USI_GOOGLE_CLOUD_PROJECT = getenv('USI_GOOGLE_CLOUD_PROJECT')
        self.USI_GOOGLE_CLOUD_BUCKET = getenv('USI_GOOGLE_CLOUD_BUCKET')
        self.USI_GOOGLE_CLOUD_PROCESSED = getenv('USI_GOOGLE_CLOUD_PROCESSED')
        self.USI_GOOGLE_CLOUD_UNPROCESSED = getenv(
            'USI_GOOGLE_CLOUD_UNPROCESSED')
        self.USI_GOOGLE_CLOUD_SERVICE_ACCOUNT = getenv(
            'USI_GOOGLE_CLOUD_SERVICE_ACCOUNT')

        self.fs = gcsfs.GCSFileSystem(
            project=self.USI_GOOGLE_CLOUD_PROJECT, token=self.USI_GOOGLE_CLOUD_SERVICE_ACCOUNT)

Here you can find all available arguments:
                https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_csv.html
                All defaults are preserved, but "index", which is set to False.
            version: If specified, should be an instance of
                ``kedro.io.core.Version``. If its ``load`` attribute is
                None, the latest version will be loaded. If its ``save``
                attribute is None, save version will be autogenerated.
            project: The GCP project. If not specified, then the default is inferred
                by a remote request.
                https://cloud.google.com/resource-manager/docs/creating-managing-projects
            gcsfs_args: Extra arguments to pass into ``GCSFileSystem``. See
                https://gcsfs.readthedocs.io/en/latest/api.html#gcsfs.core.GCSFileSystem
        """
        _credentials = deepcopy(credentials) or {}
        _gcsfs_args = deepcopy(gcsfs_args) or {}
        _gcs = gcsfs.GCSFileSystem(project=project, token=_credentials, **_gcsfs_args)
        path = _gcs._strip_protocol(filepath)  # pylint: disable=protected-access
        path = PurePosixPath("{}/{}".format(bucket_name, path) if bucket_name else path)
        super().__init__(
            filepath=path,
            version=version,
            exists_function=_gcs.exists,
            glob_function=_gcs.glob,
            load_args=load_args,
            save_args=save_args,
        )
        self._gcs = _gcs

def __init__(self, path='.', gcs=None, nfiles=10, **fsargs):
        if gcs is None:
            # minimum block size: still read on 5MB boundaries.
            self.gcs = GCSFileSystem(block_size=30 * 2 ** 20,
                                     cache_timeout=6000, **fsargs)
        else:
            self.gcs = gcs
        self.cache = SmallChunkCacher(self.gcs, nfiles=nfiles)
        self.write_cache = {}
        self.counter = 0
        self.root = path

.master(MASTER_URL)
        .config('spark.cassandra.connection.host', MORPHL_SERVER_IP_ADDRESS)
        .config('spark.cassandra.auth.username', MORPHL_CASSANDRA_USERNAME)
        .config('spark.cassandra.auth.password', MORPHL_CASSANDRA_PASSWORD)
        .config('spark.sql.shuffle.partitions', 16)
        .getOrCreate())

    log4j = spark_session.sparkContext._jvm.org.apache.log4j
    log4j.LogManager.getRootLogger().setLevel(log4j.Level.ERROR)

    save_options_usi_csv_features_raw_p_df = {
        'keyspace': MORPHL_CASSANDRA_KEYSPACE,
        'table': 'usi_csv_features_raw_p'
    }

    fs = gcsfs.GCSFileSystem(
        project=USI_GOOGLE_CLOUD_PROJECT, token=USI_GOOGLE_CLOUD_SERVICE_ACCOUNT)

    auth_provider = PlainTextAuthProvider(
        username=MORPHL_CASSANDRA_USERNAME,
        password=MORPHL_CASSANDRA_PASSWORD
    )

    cluster = Cluster(
        [MORPHL_SERVER_IP_ADDRESS], auth_provider=auth_provider)

    spark_session_cass = cluster.connect(MORPHL_CASSANDRA_KEYSPACE)

    prep_stmt_predictions_statistics = spark_session_cass.prepare(
        'INSERT INTO usi_csv_files (always_zero, day_of_data_capture, is_processed) VALUES (0, ?, false)'
    )

save_args: Additional saving options for `pyarrow.parquet.write_table`.
                Here you can find all available arguments:
                https://arrow.apache.org/docs/python/generated/pyarrow.parquet.write_table.html?highlight=write_table#pyarrow.parquet.write_table
            version: If specified, should be an instance of
                ``kedro.io.core.Version``. If its ``load`` attribute is
                None, the latest version will be loaded. If its ``save``
                attribute is None, save version will be autogenerated.
            project: The GCP project. If not specified, then the default is inferred
                by a remote request.
                https://cloud.google.com/resource-manager/docs/creating-managing-projects
            gcsfs_args: Extra arguments to pass into ``GCSFileSystem``. See
                https://gcsfs.readthedocs.io/en/latest/api.html#gcsfs.core.GCSFileSystem
        """
        _credentials = deepcopy(credentials) or {}
        _gcsfs_args = deepcopy(gcsfs_args) or {}
        _gcs = gcsfs.GCSFileSystem(project=project, token=_credentials, **_gcsfs_args)
        path = _gcs._strip_protocol(filepath)  # pylint: disable=protected-access
        path = PurePosixPath("{}/{}".format(bucket_name, path) if bucket_name else path)
        super().__init__(
            filepath=path,
            version=version,
            exists_function=_gcs.exists,
            glob_function=_gcs.glob,
            load_args=load_args,
            save_args=save_args,
        )
        self._gcs = _gcs

default file

        Args:
            parameters (dict): Parameters for the dataset to be processed. Must
                include keys "gs_bucket" (the name of the FQ bucket file path,
                for example `"gs://X/Y/Z/data.zarr"`) and "volume_name", which
                is the name of the volume in the zarr file (e.g. "raw")

        Returns:
            None
        """
        self.parameters = parameters
        self.ingest_job = self.parameters.pop("ingest_job")
        self.gs_bucket = self.parameters["gs_bucket"]
        self.volume_name = self.parameters["volume_name"]
        Zg = zarr.group(store=GCSFileSystem(token="cache").get_mapper(self.gs_bucket))
        self.vol = Zg[self.volume_name]