from layer.decorators import model

@model(name="model-name")
def cross_validate_predict(params, X, y, cv, scoring, seed=0):
    """
    params: model parameters
    X: training features
    y: training labels
    cv: validation strategy
    scoring: scoring function for evaluating each 
             fold's prediction
              
    returns model:
    """
    
    # Define base model, and pass params
    model = ....
    layer.log(params) # log parameters

    # Cross validation loop
    for fold, (train_index, val_index) in enumerate(cv.split(X, y)):
          # Fit model, evaluate and generate predictions, etc. You can also log 
          the results 
        
        
# Save predictions to submission file, you also log the dataframe

return model

# To run locally
model = cross_validate_predict(best_params, X_train, y_train, cv, scoring)

# or use layer.run([  ])