Support dynamic calculations on trained model

We should be able to support different kinds of dynamic calculations on the trained model.
At the moment some of these operations are exposed as methods on the model found inside the result object.

One example is the exploration of the model's representation of a single point in https://github.com/novonordisk-research/ProcessOptimizer/blob/develop/examples/7Dimensional_optimization_and_plotting.ipynb where the following code is executed to generate further data
```python
samples_of_y = res.models[-1].sample_y(transformed_point, n_samples=25000, random_state=None)`
```
These calculations has to be made on the server as it is the only place we have access to python. The solution currently pursued is to send the pickled result from #6 to the server and use it for further calculations

Comienza con el servidor de la REST API y el ejemplo de examples/7Dimensional_optimization_and_plotting.ipynb, especialmente con la llamada res.models[-1].sample_y(...). Revisa el enfoque de resultados serializados con pickle descrito en #6 e identifica cómo deberían exponerse los cálculos dinámicos sobre el modelo entrenado. La tarea estará terminada cuando el servidor pueda realizar los cálculos del modelo solicitados sin requerir acceso local a Python.

Escrito por el modelo de indexación a partir del texto del issue.