SKLearnProcessor unable to recognize preprocessing file in sagemaker local mode

Hello, I am experiencing an issue while using SageMaker to build a machine learning model. in Sagemaker local mode, I am facing an issue that says "python3: can't open file '/opt/ml/processing/code/processing_script.py': [Errno 2] No such file or directory." Even though the file is present in the correct location, the same code works fine in the normal mode. I am running my project in vscode dev conatiner and inside the dev container i am trying out the sagemaker local mode. Please let me know what could possibly be wrong with the following code.
### Error
```
INFO:sagemaker.local.image:docker command: docker-compose -f /tmp/tmpx5_z74cs/docker-compose.yaml up --build --abort-on-container-exit
Creating 5ktwpjv1ug-algo-1-5bua0 ...
Creating 5ktwpjv1ug-algo-1-5bua0 ... done
Attaching to 5ktwpjv1ug-algo-1-5bua0
5ktwpjv1ug-algo-1-5bua0 | python3: can't open file '/opt/ml/processing/input/code/processing_script.py': [Errno 2] No such file or directory
5ktwpjv1ug-algo-1-5bua0 exited with code 2
2
Aborting on container exit...
Traceback (most recent call last):
File "/usr/local/lib/python3.8/dist-packages/sagemaker/local/image.py", line 166, in process
_stream_output(process)
File "/usr/local/lib/python3.8/dist-packages/sagemaker/local/image.py", line 916, in _stream_output
raise RuntimeError("Process exited with code: %s" % exit_code)
RuntimeError: Process exited with code: 2

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
File "main2.py", line 20, in
processor.run(
File "/usr/local/lib/python3.8/dist-packages/sagemaker/workflow/pipeline_context.py", line 272, in wrapper
return run_func(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/sagemaker/processing.py", line 615, in run
self.latest_job = ProcessingJob.start_new(
File "/usr/local/lib/python3.8/dist-packages/sagemaker/processing.py", line 849, in start_new
processor.sagemaker_session.process(**process_args)
File "/usr/local/lib/python3.8/dist-packages/sagemaker/session.py", line 1024, in process
self._intercept_create_request(process_request, submit, self.process.__name__)
File "/usr/local/lib/python3.8/dist-packages/sagemaker/session.py", line 4813, in _intercept_create_request
return create(request)
File "/usr/local/lib/python3.8/dist-packages/sagemaker/session.py", line 1022, in submit
self.sagemaker_client.create_processing_job(**request)
File "/usr/local/lib/python3.8/dist-packages/sagemaker/local/local_session.py", line 128, in create_processing_job
processing_job.start(
File "/usr/local/lib/python3.8/dist-packages/sagemaker/local/entities.py", line 140, in start
self.container.process(
File "/usr/local/lib/python3.8/dist-packages/sagemaker/local/image.py", line 171, in process
raise RuntimeError(msg) from e
RuntimeError: Failed to run: ['docker-compose', '-f', '/tmp/tmpx5_z74cs/docker-compose.yaml', 'up', '--build', '--abort-on-container-exit']

``````
### Code
from sagemaker.local import LocalSession
from sagemaker.processing import ProcessingInput, ProcessingOutput
from sagemaker.sklearn.processing import SKLearnProcessor

sagemaker_session = LocalSession()
sagemaker_session.config = {"local": {"local_code": True}}

role = "arn:aws:iam::111111111111:role/service-role/AmazonSageMaker-ExecutionRole-20200101T000001"

processor = SKLearnProcessor(
framework_version="0.20.0", instance_count=1, instance_type="local", role=role
)

print("Starting processing job.")
print(
"Note: if launching for the first time in local mode, container image download"
" might take a few minutes to complete."
)
processor.run(
code="processing_script.py",
inputs=[
ProcessingInput(
source='./input_data/',
destination="/opt/ml/processing/input_data/",
)
],
outputs=[
ProcessingOutput(
output_name="word_count_data", source="/opt/ml/processing/processed_data/"
)
],
arguments=["job-type", "word-count"],
)

preprocessing_job_description = processor.jobs[-1].describe()
output_config = preprocessing_job_description["ProcessingOutputConfig"]

print(output_config)

for output in output_config["Outputs"]:
if output["OutputName"] == "word_count_data":
word_count_data_file = output["S3Output"]["S3Uri"]

print("Output file is located on: {}".format(word_count_data_file))

```