## For simple models to be defined in this file (e.g., in this case), no need to change load_model() function. Simply define the model inside run_model().
## For complicated saved models (e.g., created and saved using PyTorch), load them inside load_model().
## Make all changes within the two following function definitions
def load_model(): ## Do not change this line
    model = 'Dummy'
    return model

def run_model(model, data): ## Do not change this line
    import numpy as np
    np.random.seed(0)
    relevant_indices = [0, 1, 5, 2, 11, 16] ## MDTerp result should identify these feature indices as relevant with weights specified in the following line of code
    relevant_weights = [3, 5, 8, 6, 7, 10]
    data = np.average(data[:, relevant_indices], weights = relevant_weights, axis = 1)
    predict = np.exp(-data)
    prediction = np.column_stack((predict, 1 - predict ))
    return prediction ## Must return state prediction probabilities
