Almost ready to deploy docs.

Author: vbharadwaj-bk

docs/conf.py

@@ -35,5 +35,5 @@
 
 sys.path.insert(0, str(Path('..').resolve()))
 
-autodoc_mock_imports = ['torch', 'openequivariance.extlib'] 
+autodoc_mock_imports = ['torch', 'openequivariance.extlib', 'jinja2'] 
 autodoc_typehints = "description"

docs/livebuild.sh

@@ -1 +1 @@
-sphinx-autobuild . _build --watch ../openequivariance 
+sphinx-autobuild -M dirhtml . _build --watch ../openequivariance 

docs/supported_ops.rst

@@ -1,5 +1,5 @@
 Supported Operations
-==============================
+====================
 
 .. toctree::
    :maxdepth: 1
@@ -28,20 +28,6 @@ Supported Operations
      - ✅ (beta)
      - ✅ (beta)
 
-+----------------------------+----------+------------+
-| Operation                 | CUDA     | HIP        |
-+============================+==========+============+
-| UVU                      | ✅        | ✅         |
-+----------------------------+----------+------------+
-| UVW                      | ✅        | ✅         |
-+----------------------------+----------+------------+
-| UVU + Convolution        | ✅        | ✅         |
-+----------------------------+----------+------------+
-| UVW + Convolution        | ✅        | ✅         |
-+----------------------------+----------+------------+
-| Symmetric Tensor Product | ✅ (beta) | ✅ (beta)  |
-+----------------------------+----------+------------+
-
 e3nn supports a variety of connection modes for CG tensor products. We support 
 two that are commonly used in equivariant graph neural networks:
 "uvu" and "uvw". Our JIT compiled kernels should handle: