Using sweep in the train pipeline errors out

### Describe the bug or the issue that you are facing

I'm trying to implement hyperparameter tuning in the default train pipeline by setting up a sweep job. It errors out during the run-model-training-pipeline / run-pipeline after running the workflow deploy-model-training-pipeline

### Steps/Code to Reproduce

1. Replace the train_model in mlops/azureml/train/pipeline.yml with the following code (I used indents in my yml, the layout is not displayed correctly in this comment):

>
> train_model:
> name: train_model
> display_name: train-model
> type: sweep
> trial:
> code: ../../../data-science/src
> command: >-
> python train.py
> --train_data ${{inputs.train_data}}
> --model_output ${{outputs.model_output}}
> --regressor__n_estimators ${{search_space.regressor__n_estimators}}
> environment: azureml:taxi-train-env@latest
> inputs:
> train_data: ${{parent.jobs.prep_data.outputs.train_data}}
> outputs:
> model_output: ${{parent.outputs.trained_model}}
> sampling_algorithm: random
> search_space:
> regressor__n_estimators:
> type: choice
> values: [100, 200]
> objective:
> goal: minimize
> primary_metric: train_mse
> limits:
> max_total_trials: 4
> max_concurrent_trials: 2
> timeout: 7200

2. Revise the main function in data-science/src/train.py as follows:

> def main(args):
> '''Read train dataset, train model, save trained model'''
>
> # Read train data
> train_data = pd.read_parquet(Path(args.train_data))
>
> # Split the data into input(X) and output(y)
> y_train = train_data[TARGET_COL]
> X_train = train_data[NUMERIC_COLS + CAT_NOM_COLS + CAT_ORD_COLS]
>
> # Train a Random Forest Regression Model with the training set
> model = RandomForestRegressor(n_estimators = args.regressor__n_estimators,
> bootstrap = args.regressor__bootstrap,
> max_depth = args.regressor__max_depth,
> max_features = args.regressor__max_features,
> min_samples_leaf = args.regressor__min_samples_leaf,
> min_samples_split = args.regressor__min_samples_split,
> random_state=0)
>
> # log model hyperparameters
> mlflow.log_param("model", "RandomForestRegressor")
> mlflow.log_param("n_estimators", args.regressor__n_estimators)
> mlflow.log_param("bootstrap", args.regressor__bootstrap)
> mlflow.log_param("max_depth", args.regressor__max_depth)
> mlflow.log_param("max_features", args.regressor__max_features)
> mlflow.log_param("min_samples_leaf", args.regressor__min_samples_leaf)
> mlflow.log_param("min_samples_split", args.regressor__min_samples_split)
>
> # Train model with the train set
> model.fit(X_train, y_train)
>
> # Predict using the Regression Model
> yhat_train = model.predict(X_train)
>
> # Evaluate Regression performance with the train set
> r2 = r2_score(y_train, yhat_train)
> mse = mean_squared_error(y_train, yhat_train)
> rmse = np.sqrt(mse)
> mae = mean_absolute_error(y_train, yhat_train)
>
> # log model performance metrics
> mlflow.log_metric("train r2", r2)
> mlflow.log_metric("train_mse", mse)
> mlflow.log_metric("train rmse", rmse)
> mlflow.log_metric("train mae", mae)
>
> # Visualize results
> plt.scatter(y_train, yhat_train, color='black')
> plt.plot(y_train, y_train, color='blue', linewidth=3)
> plt.xlabel("Real value")
> plt.ylabel("Predicted value")
> plt.savefig("regression_results.png")
> mlflow.log_artifact("regression_results.png")
>
> # Save the model
> mlflow.sklearn.save_model(sk_model=model, path="model")
>
> from distutils.dir_util import copy_tree
>
> # copy subdirectory example
> from_directory = "model"
> to_directory = args.model_output
>
> copy_tree(from_directory, to_directory)

3. Run .github/workflows/tf-gha-deploy-infra.yml in Github Actions
4. Run .github/workflows/deploy-model-training-pipeline-classical.yml in Github Actions
5. Errors out during the run-model-training-pipeline / run-pipeline with the following msg:

Run run_id=$(az ml job create --file /home/runner/work/Azure_mlops_v2_demo/Azure_mlops_v2_demo/mlops/azureml/train/pipeline.yml --resource-group rg-mlopsv2-0040dev --workspace-name mlw-mlopsv2-0040dev --query name -o tsv)
Class WorkspaceHubOperations: This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.
Class AutoDeleteSettingSchema: This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.
Class AutoDeleteConditionSchema: This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.
Class BaseAutoDeleteSettingSchema: This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.
Class IntellectualPropertySchema: This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.
Class ProtectionLevelSchema: This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.
Class BaseIntellectualPropertySchema: This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.
ERROR: Failed to find referenced source for input binding $***parent.jobs.train_model.outputs.model_output***
Error: Process completed with exit code 1.

### Expected Output

Execute .github/workflows/deploy-model-training-pipeline-classical.yml workflow with no errors

### Versions

I'm using GitHub Actions and created my own repository following your guide and created a new dev branch.

Terraform

Azure ML CLI v2

Pre built examples from Tabular

Classic

### Which platform are you using for deploying your infrastrucutre?

GitHub Actions (GitHub)

### If you mentioned Others, please mention which platformm are you using?

_No response_

### What are you using for deploying your infrastrucutre?

Terraform

### Are you using Azure ML CLI v2 or Azure ML Python SDK v2

Azure ML CLI v2

### Describe the example that you are trying to run?

Pre built examples from Tabular