Is it Possible to use schema from ExecutionInput into container_arguments of ProcessingStep?

Hi,
Lets say I have a execution schema as follows:

```
execution_input = ExecutionInput(
schema={
"PATH_INPUT": str,
"DESTINATION_OUTPUT": str,
"study_name": str,
"ProcessingJobName": str,
"input_code": str,
"job_pk": str,
"job_sk": str,
}
)
```

How can I use the execution_input values in the Container Argument part bellow:

```
processing_step = steps.ProcessingStep(
"SageMakerProcessingJob1",
processor=get_processing_container_config(),
job_name=execution_input["ProcessingJobName"],
inputs=input_meta,
outputs=output_meta,
container_arguments=[
"--input_filename", "file.docx",
"--study_name", execution_input["study_name"]
],
container_entrypoint=["python3", "/opt/ml/processing/code/main.py"]
)
```

There the study name should come from the the execution input schema. But when trying to create the workflow graph it throughs following errors. Though in the jobname part it except the value from ExecutionInput

```
workflow_graph = steps.Chain([])
workflow = Workflow(
name="ProcessingJob3_v1",
definition=workflow_graph,
role=workflow_execution_role,
execution_input=execution_input
)
workflow.render_graph()
workflow_arn = workflow.create()
```

Error:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
in ()
----> 1 workflow.render_graph()
2 workflow_arn = workflow.create()

/home/ec2-user/SageMaker/.persisted_conda/dosjobs/lib/python3.6/site-packages/stepfunctions/workflow/stepfunctions.py in render_graph(self, portrait)
374 portrait (bool, optional): Boolean flag set to `True` if the workflow graph should be rendered in portrait orientation. Set to `False`, if the graph should be rendered in landscape orientation. (default: False)
375 """
--> 376 widget = WorkflowGraphWidget(self.definition.to_json())
377 return widget.show(portrait=portrait)
378

/home/ec2-user/SageMaker/.persisted_conda/dosjobs/lib/python3.6/site-packages/stepfunctions/steps/states.py in to_json(self, pretty)
91 return json.dumps(self.to_dict(), indent=4)
92
---> 93 return json.dumps(self.to_dict())
94
95 def __repr__(self):

/home/ec2-user/SageMaker/.persisted_conda/dosjobs/lib/python3.6/json/__init__.py in dumps(obj, skipkeys, ensure_ascii, check_circular, allow_nan, cls, indent, separators, default, sort_keys, **kw)
229 cls is None and indent is None and separators is None and
230 default is None and not sort_keys and not kw):
--> 231 return _default_encoder.encode(obj)
232 if cls is None:
233 cls = JSONEncoder

/home/ec2-user/SageMaker/.persisted_conda/dosjobs/lib/python3.6/json/encoder.py in encode(self, o)
197 # exceptions aren't as detailed. The list call should be roughly
198 # equivalent to the PySequence_Fast that ''.join() would do.
--> 199 chunks = self.iterencode(o, _one_shot=True)
200 if not isinstance(chunks, (list, tuple)):
201 chunks = list(chunks)

/home/ec2-user/SageMaker/.persisted_conda/dosjobs/lib/python3.6/json/encoder.py in iterencode(self, o, _one_shot)
255 self.key_separator, self.item_separator, self.sort_keys,
256 self.skipkeys, _one_shot)
--> 257 return _iterencode(o, 0)
258
259 def _make_iterencode(markers, _default, _encoder, _indent, _floatstr,

/home/ec2-user/SageMaker/.persisted_conda/dosjobs/lib/python3.6/json/encoder.py in default(self, o)
178 """
179 raise TypeError("Object of type '%s' is not JSON serializable" %
--> 180 o.__class__.__name__)
181
182 def encode(self, o):

TypeError: Object of type 'ExecutionInput' is not JSON serializable
```

Start with the ExecutionInput and ProcessingStep usage shown in the issue, then reproduce the failure at workflow.render_graph(), where serialization reports that ExecutionInput is not JSON serializable. Trace how job_name and container_arguments are converted into the workflow definition; done means supported execution-input values serialize correctly in container arguments and the rendered workflow is valid.

Rédigé par le modèle d'indexation à partir du texte de l'issue.