DynamicFrame.split_rows on boolean column

I am trying to create two DynamicFrames based on a column that is a boolean. I have tried
dyf.split_rows({'mybool': {'=': 'true'}}, 'is_true', 'is_not_true')
dyf.split_rows({'mybool': {'=': True}}, 'is_true', 'is_not_true')
dyf.split_rows({'mybool': {'=': 1}}, 'is_true', 'is_not_true')

The documentation at https://docs.aws.amazon.com/glue/latest/dg/aws-glue-api-crawler-pyspark-extensions-dynamic-frame.html#pyspark-split_rows-example is not helping me figure this out. For now I am using DataFrame.filter, but I was wondering if there was a way to use split_rows.

EDIT: 2024-05-28 I am adding my test code to show that I never get a count over zero in the invalid frame:

from pyspark.sql import Row

fake_data = [
    {'id': '00001', 'mybool': True},
    {'id': '00002', 'mybool': False},
    {'id': '00003', 'mybool': True},
    {'id': '00004', 'mybool': False},
    {'id': '00005', 'mybool': True},
]

df_fake_data = spark.createDataFrame(Row(**x) for x in fake_data)

df_fake_data.show()

comparison_dicts = [
    {'=': 'true'},
    {'=': True},
    {'=': 1},
    {'=': 'yes'},
    {'=': 'no'},
    {'=': '1'},
    {'=': '0'},
    {'=': 'True'},
    {'=': 'False'},
    {'==': True},
]

for comparison_dict in comparison_dicts:
    print(f'comparison_dict: {comparison_dict}')
    dyc = DynamicFrame.fromDF(df_fake_data, glueContext).split_rows(
        {'mybool': comparison_dict}, 'invalid', 'valid'
    )
    dyc.select('invalid').count()
    dyc.select('valid').count()
    dyc.select('invalid').show()
    dyc.select('valid').show()
    print()