The function begins with extracting the shape of the given input layer. As stated in previous recipes, the shape of the input layer comprises four integers: image number, image height, image width, and the number of color channels in the image. The number of features (num_features) is then evaluated using a dot-product of image height, image weight, and number of color channels.

Then, the layer is flattened or reshaped into a two-dimensional tensor (using tf$reshape). The first dimension is set to -1 (which is equal to the total number of images) and the second dimension is the number of features.

Finally, the function returns a list of flattened layers along with the total number of (input) features.