text: from d3m import index
  from d3m.metadata.base import ArgumentType
  from d3m.metadata.pipeline import Pipeline, PrimitiveStep
  
  
  # Creating pipeline
  pipeline_description = Pipeline()
  pipeline_description.add_input(name='inputs')
  
  # Step 0: dataset_to_dataframe
  step_0 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe'))
  step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
  step_0.add_output('produce')
  pipeline_description.add_step(step_0)
  
  # Step 1: column_parser
  step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.column_parser.Common'))
  step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
  step_1.add_output('produce')
  pipeline_description.add_step(step_1)
  
  # Step 2: extract_columns_by_semantic_types(attributes)
  step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common'))
  step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
  step_2.add_output('produce')
  step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,
                                    data=['https://metadata.datadrivendiscovery.org/types/Attribute'])
  pipeline_description.add_step(step_2)
  
  # Step 3: extract_columns_by_semantic_types(targets)
  step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common'))
  step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
  step_3.add_output('produce')
  step_3.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,
                                    data=['https://metadata.datadrivendiscovery.org/types/TrueTarget'])
  pipeline_description.add_step(step_3)
  
  attributes = 'steps.2.produce'
  targets = 'steps.3.produce'
  
  # Step 4: imputer
  step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.impute_missing'))
  step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes)
  step_4.add_output('produce')
  pipeline_description.add_step(step_4)
  
  # Step 5: HBOS
  step_5 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_hbos'))
  step_5.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.4.produce')
  
  step_5.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1)
  # step_5.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
  
  step_5.add_output('produce')
  pipeline_description.add_step(step_5)
  
  # Final Output
  pipeline_description.add_output(name='output predictions', data_reference='steps.5.produce')
  
  # Output to YAML
  yaml = pipeline_description.to_yaml()
  with open('pipeline.yml', 'w') as f:
      f.write(yaml)
  print(yaml)
  
  # Or you can output json
  #data = pipline_description.to_json()
  
  