before we beginthe mathematical building blocks of neural networks to understand dot-product shape compatibilityit helps to visualize the input and output tensors by aligning them as shown in figure shape(bcx = column of shape(acx shape(abz ij row of figure mat rix dot-product box diagram xyand are pictured as rectangles (literal boxes of coefficientsbecause the rows and and the columns of must have the same sizeit follows that the width of must match the height of if you go on to develop new machine-learning algorithmsyou'll likely be drawing such diagrams often more generallyyou can take the dot product between higher-dimensional tensorsfollowing the same rules for shape compatibility as outlined earlier for the case(abcd( ,-(abc(abcd(de-(abceand so on tensor reshaping third type of tensor operation that' essential to understand is tensor reshaping although it wasn' used in the dense layers in our first neural network examplewe used it when we preprocessed the digits data before feeding it into our networktrain_images train_images reshape(( )reshaping tensor means rearranging its rows and columns to match target shape naturallythe reshaped tensor has the same total number of coefficients as the initial tensor reshaping is best understood via simple examplesx np array([[ ][ ][ ]]print( shape( licensed to