Ability to pass code argument as a Pipeline variable in SageMaker Script Processor

**Describe the bug**
When creating a processing step in SageMaker Pipelines, When I try to pass the code argument for the Script processor run method and initialize the Pipeline client, I get the following error:
AttributeError: 'ParameterString' object has no attribute 'decode'

**To reproduce**
```python
#Create a Pipeline Variable
from sagemaker.workflow.parameters import (
ParameterInteger,
ParameterString,
ParameterFloat,
ParameterBoolean
)
from sagemaker.workflow.functions import Join
# Infra Parameters
processing_instance_count = ParameterInteger(name="ProcessingInstanceCount", default_value=1)
processing_instance_type = ParameterString( name="ProcessingInstanceType", default_value="ml.m5.xlarge")
processing_volume_size = ParameterInteger(name="ProcessingVolumeSize", default_value=30)

# Artifacts location Parameters
model_approval_status = ParameterString( name="ModelApprovalStatus", default_value="PendingManualApproval")
model_output_bucket = ParameterString( name="ModelOutput", default_value=f"s3://{bucket}/output/model")
train_output_bucket = ParameterString( name="TrainOutput", default_value=f"s3://{bucket}/output/train")
validation_output_bucket = ParameterString( name="ValidationOutput", default_value=f"s3://{bucket}/output/validation")
test_output_bucket = ParameterString( name="TestOutput", default_value=f"s3://{bucket}/output/test")
s3_input_data_location = ParameterString( name="S3InputDataURI", default_value=f"s3://{bucket}/ml_training/churn.csv")
s3_processing_code_location = ParameterString( name="S3ProcessingCodeLocation", default_value="s3://xxx/LightGBM-ML-Pipeline/code/8bfcc7b244a13a0833cb390dc0d312a6/churn_preprocess.py")
data_split_ratio = ParameterString( name="DataSplitRatio", default_value="0.3")

# Define Processing Step for Feature Engineering
from sagemaker.sklearn.processing import SKLearnProcessor
from sagemaker.processing import ProcessingInput, ProcessingOutput
from sagemaker.workflow.steps import ProcessingStep

framework_version = "1.0-1"
sklearn_processor = SKLearnProcessor(
framework_version=framework_version,
instance_type=processing_instance_type,
instance_count=processing_instance_count,
volume_size_in_gb = processing_volume_size,
base_job_name="sklearn-pre-process",
role=role,
sagemaker_session=pipeline_session,
)
processor_args = sklearn_processor.run(
inputs=[
ProcessingInput(source=s3_input_data_location, destination="/opt/ml/processing/input"),
],
outputs=[
ProcessingOutput(output_name="train", source="/opt/ml/processing/train",\
destination = train_output_bucket),
ProcessingOutput(output_name="validation", source="/opt/ml/processing/validation",\
destination = validation_output_bucket),
ProcessingOutput(output_name="test", source="/opt/ml/processing/test",\
destination = test_output_bucket)
],
code=s3_processing_code_location,
arguments =[
"--split-ratio",data_split_ratio
],
)
step_process = ProcessingStep(name="LightGBMDataPreProcess", step_args=processor_args)

import json
from sagemaker.workflow.pipeline import Pipeline

pipeline = Pipeline(
name=pipeline_name,
parameters=[
# Other parameters
s3_processing_code_location,
processing_volume_size,
processing_instance_count,
processing_instance_type,
training_instance_type,
training_instance_count,
model_approval_status,
model_output_bucket,
train_output_bucket,
validation_output_bucket,
test_output_bucket,
s3_input_data_location,
data_split_ratio,
],
steps=[step_process],
)

definition = json.loads(pipeline.definition())
print(definition)
```

**Expected behavior**
Ability to pass code location for Script Processors as a Pipeline variable.

**Screenshots or logs**
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[12], line 56
2 from sagemaker.workflow.pipeline import Pipeline
4 pipeline = Pipeline(
5 name=pipeline_name,
6 parameters=[
(...)
53 steps=[step_process,step_tuning], # we pass only the condition step as we have declared all steps as dependencies to the condition step
54 )
---> 56 definition = json.loads(pipeline.definition())
57 print(definition)

File /opt/conda/lib/python3.10/site-packages/sagemaker/workflow/pipeline.py:392, in Pipeline.definition(self)
385 def definition(self) -> str:
386 """Converts a request structure to string representation for workflow service calls."""
387 compiled_steps = StepsCompiler(
388 pipeline_name=self.name,
389 sagemaker_session=self.sagemaker_session,
390 steps=self.steps,
391 pipeline_definition_config=self.pipeline_definition_config,
--> 392 ).build()
394 request_dict = {
395 "Version": self._version,
396 "Metadata": self._metadata,
(...)
401 "Steps": list_to_request(compiled_steps),
402 }
404 request_dict["PipelineExperimentConfig"] = interpolate(
405 request_dict["PipelineExperimentConfig"], {}, {}, pipeline_name=self.name
406 )

File /opt/conda/lib/python3.10/site-packages/sagemaker/workflow/_steps_compiler.py:406, in StepsCompiler.build(self)
403 if self._build_count > 1:
404 raise RuntimeError("Cannot build a pipeline more than once with the same compiler.")
--> 406 return self._initialize_queue_and_build(self._input_steps)

File /opt/conda/lib/python3.10/site-packages/sagemaker/workflow/_steps_compiler.py:390, in StepsCompiler._initialize_queue_and_build(self, steps)
388 compiled_steps.append(self._build_condition_step(step))
389 else:
--> 390 compiled_steps.append(self._build_step(step))
392 self._set_serialize_output_to_json_flag(compiled_steps)
393 return compiled_steps

File /opt/conda/lib/python3.10/site-packages/sagemaker/workflow/_steps_compiler.py:323, in StepsCompiler._build_step(self, step)
316 def _build_step(self, step: Step) -> CompiledStep:
317 """Build a step."""
319 with step_compilation_context_manager(
320 pipeline_name=self.pipeline_name,
321 step_name=step.name,
322 sagemaker_session=self.sagemaker_session,
--> 323 code_hash=get_code_hash(step),
324 config_hash=get_config_hash(step),
325 pipeline_definition_config=self.pipeline_definition_config,
326 upload_runtime_scripts=self.upload_runtime_scripts,
327 upload_workspace=self.upload_workspace,
328 pipeline_build_time=self.pipeline_build_time,
329 function_step_secret_token=self._function_step_secret_token,
330 ) as context:
331 request_dict = step.to_request()
333 self.upload_runtime_scripts = context.upload_runtime_scripts

File /opt/conda/lib/python3.10/site-packages/sagemaker/workflow/utilities.py:163, in get_code_hash(step)
152 dependencies = get_processing_dependencies(
153 [
154 kwargs.get("dependencies"),
(...)
159 ]
160 )
161 code = kwargs.get("submit_app") or kwargs.get("code")
--> 163 return get_processing_code_hash(code, source_dir, dependencies)
165 if isinstance(step, TrainingStep) and step.step_args:
166 job_obj = step.step_args.func_args[0]

File /opt/conda/lib/python3.10/site-packages/sagemaker/workflow/utilities.py:218, in get_processing_code_hash(code, source_dir, dependencies)
216 # Other Processors - Spark, Script, Base, etc.
217 if code:
--> 218 code_url = urlparse(code)
219 if code_url.scheme == "" or code_url.scheme == "file":
220 return hash_files_or_dirs([code] + dependencies)

File /opt/conda/lib/python3.10/urllib/parse.py:392, in urlparse(url, scheme, allow_fragments)
372 def urlparse(url, scheme='', allow_fragments=True):
373 """Parse a URL into 6 components:
374 :///;?#
375
(...)
390 Note that % escapes are not expanded.
391 """
--> 392 url, scheme, _coerce_result = _coerce_args(url, scheme)
393 splitresult = urlsplit(url, scheme, allow_fragments)
394 scheme, netloc, url, query, fragment = splitresult

File /opt/conda/lib/python3.10/urllib/parse.py:128, in _coerce_args(*args)
126 if str_input:
127 return args + (_noop,)
--> 128 return _decode_args(args) + (_encode_result,)

File /opt/conda/lib/python3.10/urllib/parse.py:112, in _decode_args(args, encoding, errors)
110 def _decode_args(args, encoding=_implicit_encoding,
111 errors=_implicit_errors):
--> 112 return tuple(x.decode(encoding, errors) if x else '' for x in args)

File /opt/conda/lib/python3.10/urllib/parse.py:112, in (.0)
110 def _decode_args(args, encoding=_implicit_encoding,
111 errors=_implicit_errors):
--> 112 return tuple(x.decode(encoding, errors) if x else '' for x in args)

AttributeError: 'ParameterString' object has no attribute 'decode'
```

**System information**
A description of your system. Please provide:
- **SageMaker Python SDK version**: 2.215.0
- **Framework name (eg. PyTorch) or algorithm (eg. KMeans)**: Sklearn Framework processor
- **Framework version**: 1.0-1
- **Python version**: >3
- **CPU or GPU**: CPU
- **Custom Docker image (Y/N)**: N

**Additional context**
Add any other context about the problem here.