Probflow

* Add KL divergence between Discrete distribution and other continuous distributions for PyTorch (fitting models with Deterministic parameters previously wasn't working for PyTorch)
* Refactor tests - they're nice and clean now 😊
* Add autoflake to dev stack

* 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

- Fixed pytorch-only import
- Added testing matrix for python versions and pytorch/tensorflow

* Uses TensorFlow graph via tf.function - faster fitting!
* Model saving and loading (using cloudpickle)
* Fix a plotting error caused by new version of numpy/pandas
* Update TensorFlow and TensorFlow Probability version dependencies
* Add some docs and fix some other docs

* Fix backwards coefficients problem w/ `probflow.appllications.LogisticRegression`
* More examples and docs
* Add `n_parameters` and `n_variables` properties of `probflow.models.Model`
* Add `probflow.callbacks.MontiorELBO` to record ELBO loss over training
* Change `probflow.parameters.ScaleParameter` to use uniform prior (was using weird Gamma(10, 10) before?)
* Some other little bugfixes

Still awfully rough around the edges, but made some updates:

* Generative models now supported (just pass `x` to `model.fit`)
* Models save optimizer state (i.e. you can call `model.fit` once, then if you call it again later, training picks up where it left off)
* Added a multivariate normal parameter (`MultivariateNormalParameter`)
* Updated applications to work with sampling (where `n`>1)
* Started adding PyTorch support (buuuuut dunno how much I'd trust it yet)
* Expanded the examples: robust heteroscedastic regressions, Gaussian mixture models, and normalizing flows, oh my!