def get_all_regressor_coefficients(model):
    # Access the TimeNet model 
    time_net = model.model
    coefficients = {}

    # Future Regressors
    if time_net.config_regressors is not None and time_net.config_regressors.regressors is not None:
        for name, config in time_net.config_regressors.regressors.items():
            regressor_component = time_net.future_regressors
            if config.mode == 'additive':
                coefficients[name] = regressor_component.regressor_params['additive'].data.cpu().numpy()
            elif config.mode == 'multiplicative':
                coefficients[name] = regressor_component.regressor_params['multiplicative'].data.cpu().numpy()
    
    # Lagged Regressors
    if time_net.config_lagged_regressors is not None:
        for name, config in time_net.config_lagged_regressors.items():
            layer_weights = []
            for layer in time_net.covar_net:
                if isinstance(layer, nn.Linear):
                    layer_weights.append(layer.weight.data.cpu().numpy())
            if layer_weights:
                coefficients[name] = np.array(layer_weights)

    
    # Event Regressors
    if time_net.config_events is not None:
        for event, event_config in time_net.config_events.items():
            if event_config.mode == 'additive':
                coefficients[event] = time_net.event_params['additive'].data.cpu().numpy()
            elif event_config.mode == 'multiplicative':
                coefficients[event] = time_net.event_params['multiplicative'].data.cpu().numpy()

    return coefficients

# Get the regressor coefficients
coefficients = get_all_regressor_coefficients(m)
print("Coefficients:", temp_coefficients)