train_component_func in hyperparameter sweep example does not have model_output argument

### Operating System

Windows

### Version Information

This is about the tutorial
https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/pipelines/1c_pipeline_with_hyperparameter_sweep/pipeline_with_hyperparameter_sweep.ipynb

### Steps to reproduce

Examine tutorial.

### Expected behavior

Why does the `train_component_func` in the example below not contain the model_output argument? It's very confusing since the model_output argument is an input to train.py.

```
train_component_func = load_component(source="./train.yml")
score_component_func = load_component(source="./predict.yml")

# define a pipeline
@pipeline()
def pipeline_with_hyperparameter_sweep():
"""Tune hyperparameters using sample components."""
train_model = train_component_func(
data=Input(
type="uri_file",
path="wasbs://datasets@azuremlexamples.blob.core.windows.net/iris.csv",
),
c_value=Uniform(min_value=0.5, max_value=0.9),
kernel=Choice(["rbf", "linear", "poly"]),
coef0=Uniform(min_value=0.1, max_value=1),
degree=3,
gamma="scale",
shrinking=False,
probability=False,
tol=0.001,
cache_size=1024,
verbose=False,
max_iter=-1,
decision_function_shape="ovr",
break_ties=False,
random_state=42,
)
```

I understand it's technically an optional argument, but it's really confusing what happens within train.py when this argument is missing.

It would be best to just include that argument to avoid confusion.

### Actual behavior

---

### Addition information

_No response_

Open pipeline_with_hyperparameter_sweep.ipynb and compare the train_component_func call with train.yml and train.py, focusing on the model_output argument. Update the tutorial example so its component invocation matches the documented training component, then rerun or inspect the example to confirm the pipeline remains consistent.

Written by the indexing model from the issue text.