[Bug Report] Model Evaluation in abalone HPO example, for 2 best models

**Link to the notebook**
*Add the link to the notebook.*

HPO Notebook: https://github.com/aws/amazon-sagemaker-examples/blob/main/sagemaker-pipelines/tabular/tuning-step/sagemaker-pipelines-tuning-step.ipynb

This is a really useful notebook (especially for beginners). Thanks for putting this together!

**Describe the bug**
*A clear and concise description of what the bug is.*

I am trying to evaluate multiple models in the model evaluation step of the `abalone` pipeline in this notebook.

In this notebook, the top 2 models are created in the step before model evaluation (see `best_model = Model(...)` and `second_best_model = Model()`).

I want to evaluate both of these models. Currently, the notebook only evaluates the best model. There is a comment in the notebook
```python
# This can be extended to evaluate multiple models from the HPO step
```

I am trying to modify the model evaluation step of the pipeline to do this.

**To reproduce**
*A clear, step-by-step set of instructions to reproduce the bug.*

In order to evaluate 2 models, I changed the following block of code
```python
step_eval = ProcessingStep(
name="EvaluateTopModel",
processor=script_eval,
inputs=[
ProcessingInput(
source=step_tuning.get_top_model_s3_uri(top_k=0, s3_bucket=model_bucket_key),
destination="/opt/ml/processing/model",
),
ProcessingInput(
source=step_process.properties.ProcessingOutputConfig.Outputs["test"].S3Output.S3Uri,
destination="/opt/ml/processing/test",
),
],
outputs=[
ProcessingOutput(output_name="evaluation", source="/opt/ml/processing/evaluation"),
],
code="evaluate.py",
property_files=[evaluation_report],
cache_config=cache_config,
)
```
I replaced the above block of code by the following
```python
inputs=[
ProcessingInput(
source=step_tuning.get_top_model_s3_uri(top_k=0, s3_bucket=model_bucket_key),
destination="/opt/ml/processing/model",
),
ProcessingInput(
source=step_tuning.get_top_model_s3_uri(top_k=1, s3_bucket=model_bucket_key),
destination="/opt/ml/processing/model",
),
```
since k = 1 will get the second-best model. In `evaluate.py`, I replaced
```python
model_path = "/opt/ml/processing/model/model.tar.gz"
with tarfile.open(model_path) as tar:
tar.extractall(path=".")
logger.debug("Loading xgboost model.")
model = pickle.load(open("xgboost-model", "rb"))
```

by
```python
model1_path = "/opt/ml/processing/model/model.tar.gz"
with tarfile.open(model1_path) as tar:
tar.extractall(path="./model1")

model2_path = "/opt/ml/processing/model/model.tar.gz"
with tarfile.open(model1_path) as tar:
tar.extractall(path="./model2")

logger.debug("Loading xgboost model.")
model1 = pickle.load(open("model1/xgboost-model", "rb"))
model2 = pickle.load(open("model2/xgboost-model", "rb"))
```

**Logs**
*If applicable, add logs to help explain your problem. You may also attach an `.ipynb` file to this issue if it includes relevant logs or output.*

I first tried to see if I could simply change k=0 to k=1 (with no other changes anywhere) and get the pipeline to run successfully. When I run the pipeline by
- replacing k=0 by k=1 in the pipeline step (with no other changes)
- making no changes to the original `evaluate.py`

I get the following error in the Model Evaluation step
```python
>>> execution = pipeline.start()
>>> execution.list_steps()

{'StepName': 'EvaluateTopModel',
'StartTime': datetime.datetime(...),
'EndTime': datetime.datetime(...),
'StepStatus': 'Failed',
'AttemptCount': 0,
'FailureReason': 'ClientError: Cannot access S3 key.',
'Metadata': ...}
```
This suggests to me that it cannot access the s3 key for k=1. This is confusing since 2 models are clearly being created in the previous steps of the pipeline (`step_create_first` and `step_create_second`).

Second, when I run the pipeline with the exact modifications I have shown in the **To reproduce** section above
- adding an input with k=1
- changing `evaluate.py` as I described above

I get an error about not being able to handle duplicate keys. Presumably, it is having trouble with `destination="/opt/ml/processing/model",` being the same in both step inputs (k=0 and k=1).

**Question**
I seem to be having some trouble adding a second model to the model evaluation step of this pipeline. How can I modify this pipeline to evaluate the top 2 models?

Start with the HPO notebook at sagemaker-pipelines/tabular/tuning-step/sagemaker-pipelines-tuning-step.ipynb, focusing on step_eval, step_create_first, step_create_second, and the get_top_model_s3_uri calls. Then read evaluate.py and determine how both model inputs can be evaluated without duplicate destinations. Done means the pipeline evaluates the top two models successfully and produces evaluation outputs for both.

Written by the indexing model from the issue text.