E3nn

Added
- L=12 spherical harmonics

Fixed
- `TensorProduct.visualize` now works even if the TP is on the GPU.
- Github actions only trigger a push to coveralls if the corresponding token is set in github secrets.
- Batchnorm

Added
- Sparse Voxel Convolution
- Clebsch-Gordan coefficients are computed via a change of basis from the complex to real basis.
- `o3`, `nn` and `io` are accessible through `e3nn`. For instance `e3nn.o3.rand_axis_angle`.

Changed
- Since now the code is no more tested against `torch==1.8.0`, only tested against `torch>=1.10.0`

Fixed
- `wigner_3j` now _always_ returns a contiguous copy regardless of dtype or device

Fixed
- Remove `CartesianTensor._rtp`. Instead recompute the `ReducedTensorProduct` everytime. The user can save the `ReducedTensorProduct` to avoid creating it each time.
- `*equivariance_error` no longer keeps around unneeded autograd graphs
- `CartesianTensor` builds `ReducedTensorProduct` with correct device/dtype when called without one

Added
- Created module for reflected imports allowing for nice syntax for creating `irreps`, e.g. `from e3nn.o3.irreps import l3o same as Irreps("o3")`
- Add `uvu<v` mode for `TensorProduct`. Compute only the upper triangular part of the `uv` terms.
- (beta) `TensorSquare`. computes `x \otimes x` and decompose it.
- `*equivariance_error` now tell you which arguments had which error

Changed
- Give up the support of python 3.6, set `python_requires='>=3.7'` in setup
- Optimize a little bit `ReducedTensorProduct`: solve linear system only once per irrep instead of 2L+1 times.
- Do not scale line width by `path_weight` in `TensorProduct.visualize`
- `*equivariance_error` now transforms its inputs in float64 by default, regardless of the dtype used for the calculation itself

Fixed
- `ReducedTensorProduct`: replace QR decomposition by `orthonormalize` the projector `X.T X`.
This keeps `ReducedTensorProduct` deterministic because the projectors and `orthonormalize` are both deterministic.
The output of `orthonormalize` apears also to be highly sparse (luckily).

Added
- `irrep_normalization` and `path_normalization` for `TensorProduct`
- `compile_right` flag to `TensorProduct`
- Add new global flag `jit_script_fx` to optionally turn off `torch.jit.script` of fx code

Added
- Add `to_cartesian()` to `CartesianTensor`

Fixed
- make it work with `pytorch 1.10.0`