import torch


def ingest_params(user_params):
    """Creates a default parameters dictionary, overrides it if necessary
    with user selections and then returns the result.
    """
    default_params = {
                    "pool size": 50,
                    "precision": torch.float,
                    "weight initialization scheme": "Default",
                    "pre-trained": False,
                    "elite path": 'C:/model_elite.pth',
                    "grad": False
                    }
    default_params.update(user_params)  # Override with user choices
    return default_params