How to use the kedro.io.CSVLocalDataSet function in kedro

def default_csv(name):
    return CSVLocalDataSet(name)

def test_fails_with_remote_path(self, path):
        pattern = "seems to be a remote file"
        with pytest.raises(ValueError, match=pattern):
            CSVLocalDataSet(filepath=path, save_args={"sep": ","})

# a single spark partition with csv format and retrieve it with Kedro
        # CSVLocalDataSet
        temp_dir = Path(str(tmp_path / "test_data"))
        spark_data_set = SparkDataSet(
            filepath=str(temp_dir),
            file_format="csv",
            save_args={"sep": "|", "header": True},
        )
        spark_df = sample_spark_df.coalesce(1)
        spark_data_set.save(spark_df)

        single_csv_file = [
            f for f in temp_dir.iterdir() if f.is_file() and f.suffix == ".csv"
        ][0]

        csv_local_data_set = CSVLocalDataSet(
            filepath=str(single_csv_file), load_args={"sep": "|"}
        )
        pandas_df = csv_local_data_set.load()

        assert pandas_df[pandas_df["name"] == "Alex"]["age"][0] == 31

def test_load_options_csv(self, tmp_path, sample_pandas_df):
        filepath = str(tmp_path / "data")
        local_csv_data_set = CSVLocalDataSet(filepath=filepath)
        local_csv_data_set.save(sample_pandas_df)
        spark_data_set = SparkDataSet(
            filepath=filepath, file_format="csv", load_args={"header": True}
        )
        spark_df = spark_data_set.load()
        assert spark_df.filter(col("Name") == "Alex").count() == 1

def test_sequential_save_and_load(self, dummy_dataframe, filepath):
        """Tests if the correct load version is logged when two datasets are saved
        sequentially."""

        dataset1 = CSVLocalDataSet(
            filepath=filepath,
            save_args={"sep": ","},
            version=Version(None, "2000-01-01"),
        )

        dataset2 = CSVLocalDataSet(
            filepath=filepath,
            save_args={"sep": ","},
            version=Version(None, "2001-01-01"),
        )

        dataset1.save(dummy_dataframe)
        last_save_version1 = dataset1.get_last_save_version()

        dataset2.save(dummy_dataframe)
        last_save_version2 = dataset2.get_last_save_version()

        dataset2.load()
        last_load_version = dataset2.get_last_load_version()
        assert last_save_version2 == last_load_version
        assert last_save_version1 != last_save_version2

def csv_data_set(filepath, request):
    return CSVLocalDataSet(filepath=filepath, save_args=request.param)

def test_sequential_save_and_load(self, dummy_dataframe, filepath):
        """Tests if the correct load version is logged when two datasets are saved
        sequentially."""

        dataset1 = CSVLocalDataSet(
            filepath=filepath,
            save_args={"sep": ","},
            version=Version(None, "2000-01-01"),
        )

        dataset2 = CSVLocalDataSet(
            filepath=filepath,
            save_args={"sep": ","},
            version=Version(None, "2001-01-01"),
        )

        dataset1.save(dummy_dataframe)
        last_save_version1 = dataset1.get_last_save_version()

        dataset2.save(dummy_dataframe)
        last_save_version2 = dataset2.get_last_save_version()

def versioned_csv_data_set(filepath, load_version, save_version):
    return CSVLocalDataSet(
        filepath=filepath,
        save_args={"sep": ","},
        version=Version(load_version, save_version),
    )

def test_datasets_on_add(self, data_catalog_from_config):
        """Check datasets are updated correctly after adding"""
        data_catalog_from_config.add("new_dataset", CSVLocalDataSet("some_path"))
        assert isinstance(
            data_catalog_from_config.datasets.new_dataset, CSVLocalDataSet
        )
        assert isinstance(data_catalog_from_config.datasets.boats, CSVLocalDataSet)

def prepare_csv_data_with_tabs(context):
    context.read_csv_path = create_sample_csv()
    context.write_csv_path = create_temp_csv()
    context.csv_data_set = CSVLocalDataSet(
        filepath=context.read_csv_path,
        load_args={"sep": "\t"},
        save_args={"index": False, "sep": "\t"},
    )