For every tensor in the graph, we want to know it's 'taint'.
Each tensor can have the possible taints:
  Client: Input to the ML model (eg: the image input).
  Server: The weights of the model.
  ClientXServer: A tensor that is dervied after operations on both
                 client and server tensors.
  Secret_constant: A tensor that is a constant but declared as a secret.
  Public_constant: A tensor that is a constant but declared as public.

The motivation behind this analysis is to insert optimized versions of
multiplication. If one input is from server and the other from model we
can call
  ElemWiseActModelVectorMult (optimized)
otherwise we can insert a call to
  ElemWiseSecretSharedVectorMult

Matmul also expects one of its inputs to have the 'Server' taint which
this analysis identifies it.