Knowledge Gradient (one-shot), various maintenance

Breaking Changes

• Require explicit output dimensions in BoTorch models (#238)
• Make joint_optimize / sequential_optimize return acquisition function
  values (#149) [note deprecation notice below]
• standardize now works on the second to last dimension (#263)
• Refactor synthetic test functions (#273)

New Features

• Add qKnowledgeGradient acquisition function (#272, #276)
• Add input scaling check to standard models (#267)
• Add cyclic_optimize, convergence criterion class (#269)
• Add settings.debug context manager (#242)

Deprecations

• Consolidate sequential_optimize and joint_optimize into optimize_acqf
  (#150)

Bug fixes

• Properly pass noise levels to GPs using a FixedNoiseGaussianLikelihood (#241)
  [requires gpytorch > 0.3.5]
• Fix q-batch dimension issue in ConstrainedExpectedImprovement
  (6c06718)
• Fix parameter constraint issues on GPU (#260)

Minor changes

• Add decorator for concatenating pending points (#240)
• Draw independent sample from prior for each hyperparameter (#244)
• Allow dim > 1111 for gen_batch_initial_conditions (#249)
• Allow optimize_acqf to use q>1 for AnalyticAcquisitionFunction (#257)
• Allow excluding parameters in fit functions (#259)
• Track the final iteration objective value in fit_gpytorch_scipy (#258)
• Error out on unexpected dims in parameter constraint generation (#270)
• Compute acquisition values in gen_ functions w/o grad (#274)

Tests

• Introduce BotorchTestCase to simplify test code (#243)
• Refactor tests to have monolithic cuda tests (#261)