The ComputationalGraphPrimer module was created with a
modest goal in mind: its purpose is merely to serve as a
prelude to discussing automatic calculation of the loss
gradients in modern Python based platforms for deep
learning.  Most students taking classes on deep learning
focus on just using the tools provided by platforms such as
PyTorch without any understanding of how the tools really
work.  Consider, for example, Autograd --- a module that is
at the heart of PyTorch --- for automatic differentiation of
tensors. With no effort on the part of the programmer, and
through the functionality built into the torch.Tensor class,
the Autograd module keeps track of a tensor through all
calculations involving the tensor and computes its partial
derivatives with respect to the other entities involved in
the calculations.  These derivatives are subsequently used
to estimate the gradient of the loss with respect to the
learnable parameters and for backpropagating the loss.
