An MLP is used to classify the training points into $$blue$$ and $$orange$$ classes. Input features ($($x1,$1,$x2)$2)$ are transformed into

.$.$ The MLP accepts y as the input data. What is the minimum number of hidden layers needed to classify the data accurately?