Revisions of python-autoray
- update to 0.6.12: * add `torch.indices` and `tensorflow.indices` - update to 0.6.11: * Fix creation functions with builtin `like` kwarg * Allow additional keyword arguments in `tensorflow_diag` - update to 0.6.10: * The following functions now inherit default `dtype` and possibly `device` (when using torch) when the `like` kwarg is an explicit array: * `"empty"` * `"eye"` * `"full"` * `"identity"` * `"ones"` * `"zeros"` * fix NumpyMimic special attribute access * fix `"diag"` for tensorflow.
- update to 0.6.9: * `autojit`: fix jax when kwargs are used * `autojit`: simplify torch and python compiler * torch: alias min/max to amin/amax
- update to 0.6.8: * Alias jax.scipy
- update to 0.6.7: * `lazy.einsum`: allow `cotengra` or `opt_einsum` for advanced parsing, fall back to basic when neither present * add `torch.expand_dims`. * **Full Changelog**: https://github.com/jcmgray/autoray/compare/v0.6.6...v0.6.7 * fix: matmul shape for lazy by @yangguohao in #16 - Initial package release.
- update to 0.6.3: * `autoray.lazy.compute`: allow computing multiple outputs simultaneously * `autoray.lazy.Function` allow pickling and viewing of uncompiled source * make sure `shape` and `ndim` work for builtins similarly to `numpy.{shape,ndim}` * Add: `autoray.lazy.where` function for `LazyArray` * Add: `autoray.lazy.take` function for `LazyArray` * Add`LazyArray.plot_history_stats` pie charts * Add `autoray.shape` and `autoray.ndim` as preferred shape functions * Add basic support for `aesara` * `LazyArray`: fix negative axis reductions * fix fancy indexing of `LazyArray` objects
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