Batch Transformation Mismatch -

I performing batch transformation in a pipeline and joining it with the ground truth labels to create a ModelQuality monitoring step as per the guide here: https://github.com/aws/amazon-sagemaker-examples/blob/main/sagemaker-pipelines/tabular/model-monitor-clarify-pipelines/sagemaker-pipeline-model-monitor-clarify-steps.ipynb

Overview:

• Utilizing Sklearn pipeline tools for data handling and prediction (i.e. the sklearn pipeline module)
• Using my own inference script (based on the build your own example provided by AWS)
• Reading in csv file of data, which successfully loads and predicts labels (output ex: ['an01' 'hxn2' 'sv4' ... 'ngn' 'ssv' 'ssv'])
• Transformation step is unsuccessful and provides following message:

2022-11-08T16:22:59.531:[sagemaker logs]: sagemaker-us-east-1-766029086407/TEST-monitor-steps/cipm6ea9musp/projectKW-V3-Tune-TrainRegister-process/output/test/test.csv: Fail to join data: mismatched line count between the input and the output

I am having trouble understanding if the configurations of TransformInput provided to the step are wrong or if there is an error with the inference script itself.

Below is the code for the transformer step:

transformer = Transformer(
    model_name=step_create_model.properties.ModelName,
    instance_type="ml.m5.xlarge",
    instance_count=1,
    accept="text/csv",
    assemble_with="Line",
    output_path=f"s3://{bucket}/Transform",
    sagemaker_session=sagemaker_session
)

step_transform = TransformStep(
    name="TransformStep",
    transformer=transformer,
    inputs=TransformInput(
        data=preprocessing_step.properties.ProcessingOutputConfig.Outputs["test"].S3Output.S3Uri,
        join_source="Input",
        content_type="text/csv",
        split_type="Line",
    ),
)

Below is the code for the output_fn in the inference script:

def output_fn(prediction, accept):
    if accept == "application/json":
        instances = []
        for row in prediction.tolist():
            instances.append({"features": row})

        json_output = {"instances": instances}

        return worker.Response(json.dumps(json_output), mimetype=accept)
    elif accept == 'application/x-npy':
        return worker.Response(encoders.encode(prediction, accept), mimetype=accept)
    elif accept == 'text/csv':
        return worker.Response(encoders.encode(prediction, accept), mimetype=accept)
    else:
        raise RuntimeException("{} accept type is not supported by this script.".format(accept))

Thanks for the help!