def METHOD_NAME(
    auth_headers: AuthHeaders,
    hf_public_dataset_repo_csv_data: str,
) -> None:
    auth: AuthType = "none"
    expected_status_code: int = 200
    expected_error_code = None
    # TODO: add dataset with various splits, or various configs
    dataset = hf_public_dataset_repo_csv_data
    config, split = get_default_config_split()
    headers = auth_headers[auth]
    # ensure the /rows endpoint works as well
    offset = 1
    length = 10
    rows_response = poll_until_ready_and_assert(
        relative_url=f"/rows?dataset={dataset}&config={config}&split={split}&offset={offset}&length={length}",
        expected_status_code=expected_status_code,
        expected_error_code=expected_error_code,
        headers=headers,
        check_x_revision=True,
    )
    if not expected_error_code:
        content = rows_response.json()
        assert "rows" in content, rows_response
        assert "features" in content, rows_response
        rows = content["rows"]
        features = content["features"]
        assert isinstance(rows, list), rows
        assert isinstance(features, list), features
        assert len(rows) == 3, rows
        assert rows[0] == {
            "row_idx": 1,
            "row": {
                "col_1": "Vader turns round and round in circles as his ship spins into space.",
                "col_2": 1,
                "col_3": 1.0,
            },
            "truncated_cells": [],
        }, rows[0]
        assert features == [
            {"feature_idx": 0, "name": "col_1", "type": {"dtype": "string", "_type": "Value"}},
            {"feature_idx": 1, "name": "col_2", "type": {"dtype": "int64", "_type": "Value"}},
            {"feature_idx": 2, "name": "col_3", "type": {"dtype": "float64", "_type": "Value"}},
        ], features