A data engineer is refactoring DLT code that contains multiple table definitions with similar patterns:

@dlt.table(name="t1_dataset")
def t1_dataset():
    return spark.read.table("t1")

@dlt.table(name="t2_dataset")
def t2_dataset():
    return spark.read.table("t2")

@dlt.table(name="t3_dataset")
def t3_dataset():
    return spark.read.table("t3")

They attempt to parameterize the table creation using this loop:

tables = ["t1", "t2", "t3"]
for t in tables:
    @dlt.table(name=f"{t}_dataset")
    def new_table():
        return spark.read.table(t)

After running the pipeline with this refactored code, the DAG displays incorrect configuration values for these tables. What should the data engineer do to correct this?