[Bug Report] linear_learner_mnist_with_file_system_data_source notebook errors on subnet and security_group_id value type

**Link to the notebook**
[Add the link to the notebook.](https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/linear_learner_mnist/linear_learner_mnist_with_file_system_data_source.ipynb)

**Describe the bug**
This code here when run errors out indicating invalid value type.
```
linear = sagemaker.estimator.Estimator(
container,
role,
subnets=subnets,
security_group_ids=security_groups_ids,
train_instance_count=1,
train_instance_type="ml.c4.xlarge",
output_path=output_location,
sagemaker_session=sess,
)
```
The solution to this error is to make `subnets` and `security_group_ids` as array. See the solution here:

```
linear = sagemaker.estimator.Estimator(
container,
role,
subnets=[subnets],
security_group_ids=[security_groups_ids],
train_instance_count=1,
train_instance_type="ml.c4.xlarge",
output_path=output_location,
sagemaker_session=sess,
)
```

**To reproduce**
Run the above notebook using SageMaker Studio Notebook (Python 3 (Data Science)). Here is sdk version:
```
Name: sagemaker
Version: 1.72.1
---
Name: boto3
Version: 1.24.62
---
Name: botocore
Version: 1.27.62
```

Even after upgrading to v2 you get same error, but also train_* warnings.

-

**Logs**
```
Parameter image_name will be renamed to image_uri in SageMaker Python SDK v2.
---------------------------------------------------------------------------
ParamValidationError Traceback (most recent call last)
in
17 linear.set_hyperparameters(feature_dim=784, predictor_type="binary_classifier", mini_batch_size=200)
18
---> 19 linear.fit({"train": file_system_input})

/opt/conda/lib/python3.7/site-packages/sagemaker/estimator.py in fit(self, inputs, wait, logs, job_name, experiment_config)
494 self._prepare_for_training(job_name=job_name)
495
--> 496 self.latest_training_job = _TrainingJob.start_new(self, inputs, experiment_config)
497 self.jobs.append(self.latest_training_job)
498 if wait:

/opt/conda/lib/python3.7/site-packages/sagemaker/estimator.py in start_new(cls, estimator, inputs, experiment_config)
1089 train_args["enable_sagemaker_metrics"] = estimator.enable_sagemaker_metrics
1090
-> 1091 estimator.sagemaker_session.train(**train_args)
1092
1093 return cls(estimator.sagemaker_session, estimator._current_job_name)

/opt/conda/lib/python3.7/site-packages/sagemaker/session.py in train(self, input_mode, input_config, role, job_name, output_config, resource_config, vpc_config, hyperparameters, stop_condition, tags, metric_definitions, enable_network_isolation, image, algorithm_arn, encrypt_inter_container_traffic, train_use_spot_instances, checkpoint_s3_uri, checkpoint_local_path, experiment_config, debugger_rule_configs, debugger_hook_config, tensorboard_output_config, enable_sagemaker_metrics)
588 LOGGER.info("Creating training-job with name: %s", job_name)
589 LOGGER.debug("train request: %s", json.dumps(train_request, indent=4))
--> 590 self.sagemaker_client.create_training_job(**train_request)
591
592 def process(

/opt/conda/lib/python3.7/site-packages/botocore/client.py in _api_call(self, *args, **kwargs)
510 )
511 # The "self" in this scope is referring to the BaseClient.
--> 512 return self._make_api_call(operation_name, kwargs)
513
514 _api_call.__name__ = str(py_operation_name)

/opt/conda/lib/python3.7/site-packages/botocore/client.py in _make_api_call(self, operation_name, api_params)
881 }
882 request_dict = self._convert_to_request_dict(
--> 883 api_params, operation_model, context=request_context
884 )
885 resolve_checksum_context(request_dict, operation_model, api_params)

/opt/conda/lib/python3.7/site-packages/botocore/client.py in _convert_to_request_dict(self, api_params, operation_model, context)
942 )
943 request_dict = self._serializer.serialize_to_request(
--> 944 api_params, operation_model
945 )
946 if not self._client_config.inject_host_prefix:

/opt/conda/lib/python3.7/site-packages/botocore/validate.py in serialize_to_request(self, parameters, operation_model)
379 )
380 if report.has_errors():
--> 381 raise ParamValidationError(report=report.generate_report())
382 return self._serializer.serialize_to_request(
383 parameters, operation_model

ParamValidationError: Parameter validation failed:
Invalid type for parameter VpcConfig.Subnets, value: subnet-0af64674c564b501b, type: , valid types: ,
Invalid type for parameter VpcConfig.SecurityGroupIds, value: sg-0377a8692191789a7, type: , valid types: ,
```

Open introduction_to_amazon_algorithms/linear_learner_mnist/linear_learner_mnist_with_file_system_data_source.ipynb and inspect the Estimator configuration shown in the issue. Run it in SageMaker Studio with the reported setup, then verify that the subnet and security group values are accepted as collections and the training call no longer raises ParamValidationError.

Written by the indexing model from the issue text.