def METHOD_NAME(df: pd.DataFrame) -> str:
    pyarrow_schema = pyarrow_schema_from_dataframe(df=df)
    def _is_nan(value: Any) -> bool:
        if value is None:
            return True
        try:
            return np.isnan(value)
        except TypeError:
            return False
    def _format_value(row: pd.Series, schema: Dict[str, Any]) -> str:
        formated_values = []
        for row_name, row_value in row.iteritems():
            if schema[row_name].startswith("timestamp"):
                if isinstance(row_value, datetime):
                    row_value = format_datetime(row_value)
                formated_values.append(f"TIMESTAMP '{row_value}'")
            elif isinstance(row_value, list):
                formated_values.append(f"ARRAY{row_value}")
            elif isinstance(row_value, np.ndarray):
                formated_values.append(f"ARRAY{row_value.tolist()}")
            elif isinstance(row_value, tuple):
                formated_values.append(f"ARRAY{list(row_value)}")
            elif isinstance(row_value, str):
                formated_values.append(f"'{row_value}'")
            elif _is_nan(row_value):
                formated_values.append("NULL")
            else:
                formated_values.append(f"{row_value}")
        return f"({','.join(formated_values)})"
    results = df.apply(_format_value, args=(pyarrow_schema,), axis=1).tolist()
    return ",".join(results)