How to use values in a dataset to transform another dataset in code repositories

I have datasets with identical schemas stored in folders that denote an id for the dataset, e.g.:

\11111\dataset
\11112\dataset

Where the '11111' etc. indicates the dataset id. I am trying to write a transform in code repository to loop through the datasets and append them all together. The following code works for this:

def create_outputs(dataset_ids):
    transforms = []

    for id in dataset_ids:
        @transform_df(
            Output(output_path + "/appended_dataset"),
            input_path=Input(input_path + id + "/dataset"),
            )
        def compute(input_path):
            return input_path
    
        transforms.append(compute)

    return transforms

id_list = ['11111','11112']
TRANSFORMS = create_outputs(id_list)

However, rather than having the id's hardcoded in the id_list, I would like to have a separate dataset that holds the dataset id's that need to be appended. I am having difficulty getting something that works.

I have tried the following code, where the id_list_dataset holds the ids to be included in the append:

# input dataset
id_list_dataset = ["ri.foundry.main.dataset.abcdefg"]
schema = T.StructType([
    T.StructField('ID', T.StringType())
])

sc = SparkContext.getOrCreate()
rdd = sc.parallelize(id_list_dataset)
sqlContext = SQLContext(sc)
# define dataframe
temp_df = sqlContext.createDataFrame(rdd, schema)

# get list of ID's
id_list = temp_df.select('ID').collect

TRANSFORMS = create_outputs(id_list)

However, this is giving the following error: TypeError: 'method' object is not iterable