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Concept: At the core of deep learning’s success is a specialized model known as a CNN (Convolutional Neural Network). A CNN works by extracting features from images and then passing those features through a fully connected neural network to generate predictions. The feature extraction layers in the network serve to reduce the complexity of raw pixel data, transforming it into a smaller, meaningful feature set that supports accurate label prediction. CNNs are composed of multiple layers, each responsible for a specific role in feature extraction or classification. Question: Which CNN layer is described as follows? "After prior layers extract key image features, the resulting feature maps remain as multidimensional arrays of pixel values. This layer flattens the feature maps into a vector of values, which then serves as input to a fully connected layer."