Here is a Python machine learning inference script that defines a PredictorImplementation class. The __init__
constructor is already handling the loading of the model, and the _preprocess_input method has been added to map
the input schema to the expected format for the internal ML model.

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

Here is the output schema that the dictionary returned by the predict() function is expected to comply with:
```python
{{ output_schema }}
```

Here is the training code that originally produced the model:
```python
{{ training_code }}
```

Your task is to complete the _postprocess_output method im the PredictorImplementation class. The purpose of this
function is to map a single output of the internal ML model to a dictionary that complies with the output schema of
PredictorImplementation. The function should:

1. Take the output of the internal ML model as an argument
2. Return the values of the output as a dictionary in the format expected by the output schema

Only include the post-processing logic. The model loading and input pre-processing are already done, and you should not
modify those parts of the code. Also do not add any prediction code to the script. Do not change the contract of the
class. Return the full inference script, with the _postprocess_output method now implemented.