Also show fitting of the histograms to the Gaussian and Laplacian
distributions.

Sometimes the gmin/gmax in group color-normalization ends up with a zero
dimensional tensor, which needs to be accessed using .item()

Sometimes the gmin/gmax in group color-normalization ends up with a zero
dimensional tensor, which needs to be accessed using .item()

- Raise IOError instead of crude exit() when file is not found in the file-system
- Test that the correct exception is raised when opening a non-existent
checkpoint file

PyTorch 0.4 now fully supports the ONNX export features that are needed
in order to create a SummaryGraph, which is sort of a "shadow graph" for
PyTorch models.

The big advantage of SummaryGraph is that it gives us information about
the connectivity of nodes.  With connectivity information we can compute
per-node MAC (compute) and BW, and better yet, we can remove channels,
filters, and layers (more on this in future commits).

In this commit we (1) replace the long and overly-verbose ONNX node names,
with PyTorch names; and (2) move MAC and BW attributes from the Jupyter
notebook to the SummaryGraph.

Various small changes due to the chamnges in the semantics and syntax of the
PyTorch 0.4 API.

Note that currently distiller.model_performance_summary() returns wrong results
on graphs containing torch.nn.DataParallel layers.

fix path to the resnet20 checkpoint in one of the examples