Conditional parameter spec for hyperparameter tuning for logistic regression

[revanthps]

How to configure conditonalparameterspec for the below code where, when penalty is l2 solver should be 'sag' and when penalty is 'l1', solver should be 'saga'.

from google.cloud.aiplatform import hyperparameter_tuning as hpt

worker_pool_specs = [
        {
            "machine_spec": {
                "machine_type": "n1-standard-4",
                "accelerator_type": "NVIDIA_TESLA_K80",
                "accelerator_count": 1,
            },
            "replica_count": 1,
            "container_spec": {
                "image_uri": container_image_uri,
                "command": [],
                "args": [],
            },
        }
    ]

custom_job = aiplatform.CustomJob(
    display_name='my_job',
    worker_pool_specs=worker_pool_specs,
    labels={'my_key': 'my_value'},
)


hp_job = aiplatform.HyperparameterTuningJob(
    display_name='hp-test',
    custom_job=job,
    metric_spec={
        'loss': 'minimize',
    },
    parameter_spec={
        'C': hpt.DoubleParameterSpec(min=0.001, max=0.1, scale='log'),
        'max_iter': hpt.IntegerParameterSpec(min=4, max=128, scale='linear'),
        'penalty': hpt.CategoricalParameterSpec(values=['l1', 'l2']),
        'solver': hpt.CategoricalParameterSpec(values=['sag', 'saga'])
    },
    max_trial_count=128,
    parallel_trial_count=8,
    labels={'my_key': 'my_value'},
    )

hp_job.run()

print(hp_job.trials)

I have looked into documentations and have tried asking help from GPTs but am not able to figure it out. Could you guys help?