text: from azure.ai.ml import dsl, Input, command, Output
  from azure.ai.ml.automl import regression
  from azure.ai.ml.entities import PipelineJob
  from pathlib import Path
  
  from azure.ai.ml.entities._job.automl.tabular import TabularFeaturizationSettings
  
  input_data_dir = str(Path(__file__).parent.parent.parent / "automl_job/test_datasets/house_pricing")
  
  
  def generate_dsl_pipeline() -> PipelineJob:
      # Construct pipeline
      @dsl.pipeline(
          default_compute="cpu-cluster",
          description="Example of using automl function inside pipeline",
      )
      def automl_node_in_pipeline(
              automl_train_data,
              automl_validate_data,
              automl_test_data
      ):
          hello_automl_regression = regression(
              training_data=automl_train_data,
              validation_data=automl_validate_data,
              test_data=automl_test_data,
              target_column_name="SalePrice",
              primary_metric="r2_score",
              featurization=TabularFeaturizationSettings(mode="Off"),
              outputs={"best_model": Output(type="mlflow_model")}
          )
          hello_automl_regression.set_limits(max_trials=1, max_concurrent_trials=1)
          hello_automl_regression.set_training(
              enable_stack_ensemble=False,
              enable_vote_ensemble=False
          )
  
          command_func = command(
              inputs=dict(
                  automl_output=Input(type="mlflow_model")
              ),
              command="ls ${{inputs.automl_output}}",
              environment="azureml:AzureML-Minimal:1"
          )
          show_output = command_func(automl_output=hello_automl_regression.outputs.best_model)
          return hello_automl_regression.outputs
  
      pipeline = automl_node_in_pipeline(
          automl_train_data=Input(path=input_data_dir + "/train", type="mltable"),
          automl_validate_data=Input(path=input_data_dir + "/valid", type="mltable"),
          automl_test_data=Input(path=input_data_dir + "/test", type="mltable")
      )
      return pipeline
  