How to use the mlblocks.MLPipeline function in mlblocks

def test_mlpipeline(self):
        primitives = [
            'sklearn.ensemble.RandomForestClassifier'
        ]
        init_params = {
            'sklearn.ensemble.RandomForest#1': {
                'n_estimators': 500
            }
        }

        pipeline = MLPipeline(primitives=primitives, init_params=init_params)
        pipeline2 = MLPipeline(pipeline)

        assert pipeline2.primitives == ['sklearn.ensemble.RandomForestClassifier']
        assert pipeline2.init_params == {
            'sklearn.ensemble.RandomForest#1': {
                'n_estimators': 500
            }

def test_none(self):
        primitives = [
            'sklearn.ensemble.RandomForestClassifier'
        ]
        init_params = {
            'sklearn.ensemble.RandomForest#1': {
                'n_estimators': 500
            }
        }

        pipeline = MLPipeline(primitives=primitives, init_params=init_params)

        assert pipeline.primitives == ['sklearn.ensemble.RandomForestClassifier']
        assert pipeline.init_params == {
            'sklearn.ensemble.RandomForest#1': {
                'n_estimators': 500
            }

def test_mlpipeline(self):
        primitives = [
            'sklearn.ensemble.RandomForestClassifier'
        ]
        init_params = {
            'sklearn.ensemble.RandomForest#1': {
                'n_estimators': 500
            }
        }

        pipeline = MLPipeline(primitives=primitives, init_params=init_params)
        pipeline2 = MLPipeline(pipeline)

        assert pipeline2.primitives == ['sklearn.ensemble.RandomForestClassifier']
        assert pipeline2.init_params == {
            'sklearn.ensemble.RandomForest#1': {
                'n_estimators': 500
            }

try:
            primitive_path = os.path.join(MLBLOCKS_PRIMITIVES, primitive_filename)
            with open(primitive_path, 'r') as f:
                primitive = json.load(f)

            primitive_name = primitive['name']
            fixed_hyperparameters = primitive.get('hyperparameters', dict()).get('fixed', dict())

            init_hyperparameters = dict()
            for name, hyperparameter in fixed_hyperparameters.items():
                if 'default' not in hyperparameter:
                    type_ = hyperparameter.get('type')
                    init_hyperparameters[name] = HYPERPARAMETER_DEFAULTS.get(type_)

            block_name = primitive_name + '#1'
            mlpipeline = MLPipeline(
                primitives=[primitive_name],
                init_params={block_name: init_hyperparameters}
            )

            # Validate methods
            mlblock = mlpipeline.blocks[block_name]
            if mlblock._class:
                fit = primitive.get('fit')
                if fit:
                    assert hasattr(mlblock.instance, fit['method'])

                produce = primitive['produce']
                assert hasattr(mlblock.instance, produce['method'])

        except Exception:
            raise ValueError("Invalid JSON primitive: {}".format(primitive_filename))

'n_estimators': 500
                }
            },
            'input_names': {
                'sklearn.ensemble.RandomForest#1': {
                    'X': 'X1'
                }
            },
            'output_names': {
                'sklearn.ensemble.RandomForest#1': {
                    'y': 'y1'
                }
            }
        }

        pipeline = MLPipeline(pipeline_dict)

        assert pipeline.primitives == ['sklearn.ensemble.RandomForestClassifier']
        assert pipeline.init_params == {
            'sklearn.ensemble.RandomForest#1': {
                'n_estimators': 500
            }
        }
        assert pipeline.input_names == {
            'sklearn.ensemble.RandomForest#1': {
                'X': 'X1'
            }
        }
        assert pipeline.output_names == {
            'sklearn.ensemble.RandomForest#1': {
                'y': 'y1'
            }

def test_list(self):
        primitives = [
            'sklearn.ensemble.RandomForestClassifier'
        ]
        init_params = {
            'sklearn.ensemble.RandomForest#1': {
                'n_estimators': 500
            }
        }

        pipeline = MLPipeline(primitives, init_params=init_params)

        assert pipeline.primitives == ['sklearn.ensemble.RandomForestClassifier']
        assert pipeline.init_params == {
            'sklearn.ensemble.RandomForest#1': {
                'n_estimators': 500
            }