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Also fix a few lint errors in unit tests.

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Add AstroWaveNet model, a generative model of astronomical light curves. AstroWaveNet's hidden states can be used in a semi-supervised fashion for downstream tasks like finding exoplanets.

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Factor out evaluate(), continuous_eval(), and continuous_train_and_eval() from estimator_util.py into estimator_runner.py.

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1. reshard_arrays(xs, ys): Reshards arrays in xs to match the lengths of arrays in ys.
2. uniform_cadence_light_curve(): Combines data into a single light curve with uniform cadence numbers.

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Remove the constant LONG_CADENCE_TIME_DELTA_DAYS as this is not strictly constant between light curves (although it is very close).

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This applies mainly to scrambled data (NaN time values typically come with NaN flux values, which are removed anyway, but scrambing decouples NaN time values from NaN flux values).

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Update configurations with label mapping for simulated data and new batch_size and learning_rate from vizier studies.

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Ensure consistent argument order, change NaN processing to remove NaN times as well, and do so before scrambling

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Modify processing pipeline to enable generation of scrambled lightcurves. Fix bugs to enable generation of inverted lightcurves.

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Inversion should be done *after* fitting a normalization curve to the stellar variability and dividing it away. Doing it before normalizing may result in unintentionally reversing the inversion. Therefore we remove the option to do it at this point.

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This subcase may pass or fail based on the version of pydl.

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- Return additional metadata about the spline fit: likelihood_term, penalty_term, bic.
- Don't fit a spline to < 4 points. Previously, light curve segments with less than 4 points would be fit by a fake spline that fit the points exactly.
- Better support for light curve segments with 0-2 points.

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