High Order GP model, multi-step look-ahead acquisition function

Compatibility

• Require PyTorch >=1.7.1 (#714).
• Require GPyTorch >=1.4 (#714).

New Features

• HigherOrderGP - High-Order Gaussian Process (HOGP) model for
  high-dimensional output regression (#631, #646, #648, #680).
• qMultiStepLookahead acquisition function for general look-ahead
  optimization approaches (#611, #659).
• ScalarizedPosteriorMean and project_to_sample_points for more
  advanced MFKG functionality (#645).
• Large-scale Thompson sampling tutorial (#654, #713).
• Tutorial for optimizing mixed continuous/discrete domains (application
  to multi-fidelity KG with discrete fidelities) (#716).
• GPDraw utility for sampling from (exact) GP priors (#655).
• Add X as optional arg to call signature of MCAcqusitionObjective (#487).
• OSY synthetic test problem (#679).

Bug Fixes

• Fix matrix multiplication in scalarize_posterior (#638).
• Set X_pending in get_acquisition_function in qEHVI (#662).
• Make contextual kernel device-aware (#666).
• Do not use an MCSampler in MaxPosteriorSampling (#701).
• Add ability to subset outcome transforms (#711).

Performance Improvements

• Batchify box decomposition for 2d case (#642).

Other Changes

• Use scipy distribution in MES quantile bisect (#633).
• Use new closure definition for GPyTorch priors (#634).
• Allow enabling of approximate root decomposition in posterior calls (#652).
• Support for upcoming 21201-dimensional PyTorch SobolEngine (#672, #674).
• Refactored various MOO utilities to allow future additions (#656, #657, #658, #661).
• Support input_transform in PairwiseGP (#632).
• Output shape checks for t_batch_mode_transform (#577).
• Check for NaN in gen_candidates_scipy (#688).
• Introduce base_sample_shape property to Posterior objects (#718).