Vertex AI Pipelines (Kubeflow) skip step with dependent outputs on later step

I’m trying to run a Vertex AI Pipelines job where I skip a certain pipeline step if the value of a certain pipeline parameter (in this case do_task1) is False. But because there is another step that runs unconditionally and expects the output of the first potentially skipped step, I get the following error, independently of do_task1 being True or False:

AssertionError: component_input_artifact: pipelineparam--task1-output_path not found. All inputs: parameters {
  key: "do_task1"
  value {
    type: STRING
  }
}
parameters {
  key: "task1_name"
  value {
    type: STRING
  }
}

It seems like the compiler just cannot find the output output_path from task1. So I wonder if there is any way to have some sort of placeholders for the outputs of those steps that are under a dsl.Condition , and thus they get filled with default values unless the actual steps run and fill them with the non-default values. The code below represents the problem and is easily reproducible.

I'm using google-cloud-aiplatform==1.14.0 and kfp==1.8.11

from typing import NamedTuple

from kfp import dsl
from kfp.v2.dsl import Dataset, Input, OutputPath, component
from kfp.v2 import compiler

from google.cloud.aiplatform import pipeline_jobs

@component(
    base_image="python:3.9",
    packages_to_install=["pandas"]
)
def task1(
    # inputs
    task1_name: str,
    # outputs
    output_path: OutputPath("Dataset"),
) -> NamedTuple("Outputs", [("output_1", str), ("output_2", int)]):

    import pandas as pd
    
    output_1 = task1_name + "-processed"
    output_2 = 2

    df_output_1 = pd.DataFrame({"output_1": [output_1]})
    df_output_1.to_csv(output_path, index=False)

    return (output_1, output_2)

@component(
    base_image="python:3.9",
    packages_to_install=["pandas"]
)
def task2(
    # inputs
    task1_output: Input[Dataset],
) -> str:

    import pandas as pd

    task1_input = pd.read_csv(task1_output.path).values[0][0]

    return task1_input

@dsl.pipeline(
    pipeline_root='pipeline_root',
    name='pipelinename',
)
def pipeline(
    do_task1: bool,
    task1_name: str,
):

    with dsl.Condition(do_task1 == True):

        task1_op = (
            task1(
                task1_name=task1_name,
            )
        )

    task2_op = (
        task2(
            task1_output=task1_op.outputs["output_path"],
        )
    )


if __name__ == '__main__':
    
    do_task1 = True # <------------ The variable to modify ---------------

    # compile pipeline
    compiler.Compiler().compile(
        pipeline_func=pipeline, package_path='pipeline.json')

    # create pipeline run
    pipeline_run = pipeline_jobs.PipelineJob(
        display_name='pipeline-display-name',
        pipeline_root='pipelineroot',
        job_id='pipeline-job-id',
        template_path='pipelinename.json',
        parameter_values={
            'do_task1': do_task1, # pipeline compilation fails with either True or False values
            'task1_name': 'Task 1',
        },
        enable_caching=False
    )
    
    # execute pipeline run
    pipeline_run.run()

Any help is much appreciated!