Here is a Python machine learning inference module that defines a PredictorImplementation class. In the code, we have
defined model loading in the __init__ constructor as well as the logic for performing a prediction with the model
when predict() is called.

```python
{{ inference_code }}
```

However, this script has been worked on by multiple engineers, and it is possible that there are discrepancies. Your
task is to ensure the script is logically sound and that it adheres to the required structure.

The PredictorImplementation class extends the Predictor abstract base class. Here is the definition of Predictor:
```python
{{ predictor_interface_source }}
```

Check for the following:
1. PredictorImplementation should be a valid subclass of Predictor
2. The model loading code should be in the __init__ method; no model loading at prediction time
3. The predict() method should return a prediction when called with an input
4. The _preprocess_input() and _postprocess_output() methods should be defined and used correctly
5. The code should be organized logically
6. Handle any necessary error checking and validation

Do not completely rewrite the script. Only make target changes, and only if you are VERY confident that there is an
error in the implementation. At the end, return the full inference script with all your changes (if any).