code: def predict(parameters, X):
      
  
      # 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
docs: 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)