Fix typo (#2235)

Co-authored-by: Chao Ma <mctt90@gmail.com>

python/dgl/nn/pytorch/conv/gatconv.py

@@ -33,7 +33,7 @@ class GATConv(nn.Module):
     ----------
     in_feats : int, or pair of ints
         Input feature size; i.e, the number of dimensions of :math:`h_i^{(l)}`.
-        ATConv can be applied on homogeneous graph and unidirectional
+        GATConv can be applied on homogeneous graph and unidirectional
         `bipartite graph <https://docs.dgl.ai/generated/dgl.bipartite.html?highlight=bipartite>`__.
         If the layer is to be applied to a unidirectional bipartite graph, ``in_feats``
         specifies the input feature size on both the source and destination nodes.  If

python/dgl/nn/pytorch/conv/nnconv.py

@@ -28,7 +28,7 @@ class NNConv(nn.Module):
     ----------
     in_feats : int
         Input feature size; i.e, the number of dimensions of :math:`h_j^{(l)}`.
-        NN can be applied on homogeneous graph and unidirectional
+        NNConv can be applied on homogeneous graph and unidirectional
         `bipartite graph <https://docs.dgl.ai/generated/dgl.bipartite.html?highlight=bipartite>`__.
         If the layer is to be applied on a unidirectional bipartite graph, ``in_feats``
         specifies the input feature size on both the source and destination nodes.  If

python/dgl/nn/pytorch/conv/sageconv.py

@@ -30,7 +30,7 @@ class SAGEConv(nn.Module):
     in_feats : int, or pair of ints
         Input feature size; i.e, the number of dimensions of :math:`h_i^{(l)}`.
 
-        GATConv can be applied on homogeneous graph and unidirectional
+        SAGEConv can be applied on homogeneous graph and unidirectional
         `bipartite graph <https://docs.dgl.ai/generated/dgl.bipartite.html?highlight=bipartite>`__.
         If the layer applies on a unidirectional bipartite graph, ``in_feats``
         specifies the input feature size on both the source and destination nodes.  If