Torchsde

Two-and-a-half-years since the last release. How time flies. This is primarily a maintenance release.

Efficiency improvements:
- add `f_and_g` and `f_and_g_prod` functions so that drift and diffusion can be computed together

Bug fixes:
- calling `solver.integrate` twice internally
- adaptive error estimation not recognizing singleton tensors

New features include
- BrownianInterval: A Brownian motion data structure that has constant memory storage and exact queries relying on LRU caches
- Basic solvers for Stratonovich SDEs
- Full adjoint support for Ito and Stratonovich SDEs for all noise types declared in the codebase
- Various Python performance enhancements

- Add new Brownian motion classes with faster query speed based on PyTorch C++ API.
- The new Brownian motion classes have the same API as existing ones, so they serve as direct replacements.
- Importing these classes is as simple as `from torchsde.brownian_lib import BrownianPath, BrownianTree`.
- The old Brownian motion classes written in pure Python are not yet deprecated, and likely won't be deprecated in the near future.
- Add type hints for functions of the public API.

This codebase provides stochastic differential equation (SDE) solvers with GPU support and efficient sensitivity analysis.