This example split-trains a neural network to classify 10 different categories
of images using the CIFAR-10 database of 60,000 32x32 color images.

First, the CIFAR-10 dataset of 60,000 images is partitioned into two independent
datasets of 25,000 and one test set of 10,000 images.  Each of these datasets
is saved into two Package files, a specially structured zip archive file which
holds the images along with labels for training.

Each Package file is placed on an independent Access Point.  These represent
two independent organizations with similar data who wish to jointly build a
network without sharing their data.

A neural network structure is defined similar to LeNet-5, including a split
point.  The split training process begins, alternating between Access Points.
At the end of the process, Organization One -- who initiated the training --
owns the trained neural network.  The network resides on their Access Point.
At the end of training this network is also pulled down locally as a PyTorch
.pth file.

The final scripts demonstrate using the trained network for inference.  A
local inference uses the .pth file to categorize the test images.  The FED
inference performs classification securely by moving the algorithm to one
organization's Access point to execute on their data.

The last examples use SMPC for the highest level of secure computation,
protecting both the algorithm and the data during processing.  One runs a
single image file inference, the other builds a batch.
