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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def predict(parameters, X):
 """ Using the learned parameters, predicts a class for each example in X Arguments: parameters -- python dictionary containing your parameters X -- input data of size (n_x, m) Returns predictions -- vector of predictions of our model (red: 0 / blue: 1) """


    # Computes probabilities using forward propagation,
    # and classifies to 0/1 using 0.5 as the threshold.
    A2, cache = forward_propagation(X, parameters)
    predictions = np.array([1 if (i > 0.5) else 0 for i in A2[0]])

    return predictions