remove nn folders

python/cugraph-dgl/cugraph_dgl/nn/conv/base.py

@@ -277,112 +277,3 @@ def to(self, device: Union[torch.device, str, int]) -> "cugraph_dgl.nn.SparseGra
         )
 
         return sg
-
-
-def conditional_class(import_name):
-    def decorator(cls):
-        try:
-            __import__(import_name)
-            return cls
-        except ImportError:
-            return None
-
-    return decorator
-
-
-@conditional_class("pylibcugraphops")
-class BaseConv(torch.nn.Module):
-    r"""An abstract base class for cugraph-ops nn module."""
-
-    def __init__(self):
-        super().__init__()
-
-    def reset_parameters(self):
-        r"""Resets all learnable parameters of the module."""
-        raise NotImplementedError
-
-    def forward(self, *args):
-        r"""Runs the forward pass of the module."""
-        raise NotImplementedError
-
-    def get_cugraph_ops_CSC(
-        self,
-        g: Union[SparseGraph, dgl.DGLHeteroGraph],
-        is_bipartite: bool = False,
-        max_in_degree: Optional[int] = None,
-    ) -> "ops_torch.CSC":
-        """Create CSC structure needed by cugraph-ops."""
-
-        if not isinstance(g, (SparseGraph, dgl.DGLHeteroGraph)):
-            raise TypeError(
-                f"The graph has to be either a 'cugraph_dgl.nn.SparseGraph' or "
-                f"'dgl.DGLHeteroGraph', but got '{type(g)}'."
-            )
-
-        # TODO: max_in_degree should default to None in pylibcugraphops
-        if max_in_degree is None:
-            max_in_degree = -1
-
-        if isinstance(g, SparseGraph):
-            offsets, indices, _ = g.csc()
-        else:
-            offsets, indices, _ = g.adj_tensors("csc")
-
-        graph = ops_torch.CSC(
-            offsets=offsets,
-            indices=indices,
-            num_src_nodes=g.num_src_nodes(),
-            dst_max_in_degree=max_in_degree,
-            is_bipartite=is_bipartite,
-        )
-
-        return graph
-
-    def get_cugraph_ops_HeteroCSC(
-        self,
-        g: Union[SparseGraph, dgl.DGLHeteroGraph],
-        num_edge_types: int,
-        etypes: Optional[torch.Tensor] = None,
-        is_bipartite: bool = False,
-        max_in_degree: Optional[int] = None,
-    ) -> "ops_torch.HeteroCSC":
-        """Create HeteroCSC structure needed by cugraph-ops."""
-
-        if not isinstance(g, (SparseGraph, dgl.DGLHeteroGraph)):
-            raise TypeError(
-                f"The graph has to be either a 'cugraph_dgl.nn.SparseGraph' or "
-                f"'dgl.DGLHeteroGraph', but got '{type(g)}'."
-            )
-
-        # TODO: max_in_degree should default to None in pylibcugraphops
-        if max_in_degree is None:
-            max_in_degree = -1
-
-        if isinstance(g, SparseGraph):
-            offsets, indices, etypes = g.csc()
-            if etypes is None:
-                raise ValueError(
-                    "SparseGraph must have 'values' to create HeteroCSC. "
-                    "Pass in edge types as 'values' when creating the SparseGraph."
-                )
-            etypes = etypes.int()
-        else:
-            if etypes is None:
-                raise ValueError(
-                    "'etypes' is required when creating HeteroCSC "
-                    "from dgl.DGLHeteroGraph."
-                )
-            offsets, indices, perm = g.adj_tensors("csc")
-            etypes = etypes[perm].int()
-
-        graph = ops_torch.HeteroCSC(
-            offsets=offsets,
-            indices=indices,
-            edge_types=etypes,
-            num_src_nodes=g.num_src_nodes(),
-            num_edge_types=num_edge_types,
-            dst_max_in_degree=max_in_degree,
-            is_bipartite=is_bipartite,
-        )
-
-        return graph