We are writing Python code to expose a ML model for inference. The model is wrapped in a class called
"PredictorImplementation". Here is some context about the model:

## Output Schema
The "predict()" function in PredictorImplementation must return a dictionary compliant with this schema:
```python
{{ output_schema }}
```

## Input Schema
The "predict()" function in PredictorImplementation expects an input dictionary compliant with this schema:
```python
{{ input_schema }}
```

## Original Training Code
The model was trained using the following code:
```python
{{ training_code }}
```

## Inference Script (Work in Progress)
Here is the inference script that we are working on:
```python
{{ inference_code }}
```

## Task
Your task is to complete the "predict()" function in the inference script. Note that the model loading is already
done in the __init__ constructor, and input pre-processing and output post-processing functions have already been
implemented in "_preprocess_input()" and "_postprocess_output()". These functions handle mapping the input/output
schemas of PredictorImplementation to the input/output formats expected by the inner model. Your job is to
implement the prediction logic. The function should:

1. Take the pre-processed input using the _preprocess_input method
2. Use the loaded model to make predictions
3. Format the predictions according to the output schema using the _postprocess_output method
4. Return an output matching the output schema

Do not modify the model loading code or other existing parts of the code, only implement the prediction logic. Return
the full inference script, with the "predict()" function completed.