I have a working dbt python incremental model as follows:

import pyspark.sql.functions as F
from pyspark.sql import DataFrame
from pyspark.sql.window import Window


def calculate_final_customer_score(
    combined_df: DataFrame, individual_scores: list
) -> DataFrame:
    """
    Calculate the final customer score based on individual scores with equal weights.
    Args:
    combined_df (DataFrame): DataFrame containing the combined customer data.
    individual_scores (list): List of individual score columns.
    Returns:
    DataFrame: DataFrame with the final customer score column.
    """

    # Calculate the weighted sum of individual scores
    weighted_sum = sum(F.col(score) for score in individual_scores)

    # Calculate the final customer score
    final_customer_score = weighted_sum / len(individual_scores)

    combined_df = combined_df.withColumn("final_customer_score", final_customer_score)

    return combined_df


def model(dbt, session):
    dbt.config(materialized="incremental", partition_by=["execution_date"])

    # Reference the source table
    combined_df = dbt.ref("combined_proxy_2023_per_cust_v2")

    # Define individual scores
    individual_scores = ["rfm_score", "growth_score_adjusted", "points_score_adjusted"]

    if dbt.is_incremental:
        # Get the max execution_date from the current table
        max_execution_date_query = f"SELECT MAX(execution_date) FROM {dbt.this}"
        max_execution_date = session.sql(max_execution_date_query).collect()[0][0]

        # Filter for new rows since the last execution
        combined_df = combined_df.filter(F.col("execution_date") > max_execution_date)

    # Calculate final customer score with equal weights
    combined_df = calculate_final_customer_score(combined_df, individual_scores)

    # Calculate deciles
    combined_df = combined_df.withColumn(
        "decile", F.ntile(10).over(Window.orderBy(F.col("final_customer_score")))
    )

    # Sort the decile tables in descending order by decile
    combined_df = combined_df.orderBy(F.col("decile").desc())

    return combined_df

I am trying to specify the partition column but unable to do so…
The model builds successfully but when I try to check if the partition_by is working using

spark.sql("SHOW Partitions adi.inst__davi_dev.weighted_avg_target_per_cust").show()

I got the following:

[DELTA_SHOW_PARTITION_IN_NON_PARTITIONED_TABLE] SHOW PARTITIONS is not allowed on a table that is not partitioned: `inst__davi_dev`.`weighted_avg_target_per_cust` SQLSTATE: 42809

Meaning the partition is not applied. But I am unable to understand as what went wrong here? This is my first time working with partition_by in dbt python model.
Any help/ suggestions/ guidance will help!!