Add probabalistic kwarg to Dense, DenseRegression, and Embedding

[brendanhasz]

This adds a probabilistic kwarg to the Dense, DenseRegression, and Embedding modules. When probabilistic=True, Normal distributions are used for the variational posteriors, but when probabilistic=False, Deterministic distributions are used instead.

Also updates some callbacks to track walltime, adds calibration methods (calibration_curve, calibration_curve_plot, and expected_calibration_error) to ContinuousModel, and adds a Neural Linear Model example which uses these features.

Will fix https://github.com/brendanhasz/probflow/issues/18

So, all in all:

• Add a probabilistic keyword argument to Dense, DenseRegression, and Embedding modules.
• Update MonitorMetric and MonitorELBO to also track walltime
• Add calibration methods to ContinuousModel: calibration_curve, calibration_curve_plot, and expected_calibration_error
• Add support for MultivariateNormalParameter for PyTorch (by implementing probflow.utils.ops.log_cholesky_transform for pytorch)
• Add a Neural linear example which uses most of these new features