How to use the stepfunctions.workflow.Workflow function in stepfunctions

def create_workflow_and_check_definition(workflow_graph, workflow_name, sfn_client, sfn_role_arn):
    # Create workflow
    workflow = Workflow(
        name=workflow_name,
        definition=workflow_graph,
        role=sfn_role_arn,
        client=sfn_client
    )
    state_machine_arn = workflow.create()

    # Check workflow definition
    state_machine_desc = sfn_client.describe_state_machine(stateMachineArn=state_machine_arn)
    assert workflow.definition.to_dict() == json.loads(state_machine_desc.get('definition'))

    return workflow

"ParamE": test_step_01.output()["Response"]["Key04"]
        }
    )

    test_step_03 = Pass(
        state_id='StateThree',
        parameters={
            'ParamG': "SampleValueG",
            "ParamF": execution_input["Key06"],
            "ParamH": "SampleValueH",
            "ParamI": test_step_02.output()
        }
    )

    workflow_definition = Chain([test_step_01, test_step_02, test_step_03])
    workflow = Workflow(
        name='TestWorkflow',
        definition=workflow_definition,
        role='testRoleArn',
        execution_input=execution_input,
        client=client
    )
    return workflow

"""
        self.preprocessor = preprocessor
        self.estimator = estimator
        self.inputs = inputs
        self.s3_bucket = s3_bucket

        for key in self.__class__.__allowed_kwargs:
            setattr(self, key, kwargs.pop(key, None))

        if not self.pipeline_name:
            self.pipeline_name = 'inference-pipeline-{date}'.format(date=self._generate_timestamp())

        self.definition = self.build_workflow_definition()
        self.input_template = self._extract_input_template(self.definition)

        workflow = Workflow(name=self.pipeline_name, definition=self.definition, role=role, format_json=True, client=client)

        super(InferencePipeline, self).__init__(s3_bucket=s3_bucket, workflow=workflow, role=role, client=client)