chore: import upstream snapshot with attribution

easygraph/model/hypergraphs/hgnn.py

@@ -0,0 +1,45 @@
+import torch
+import torch.nn as nn
+
+from easygraph.nn import HGNNConv
+
+
+class HGNN(nn.Module):
+    r"""The HGNN model proposed in `Hypergraph Neural Networks <https://arxiv.org/pdf/1809.09401>`_ paper (AAAI 2019).
+
+    Parameters:
+        ``in_channels`` (``int``): :math:`C_{in}` is the number of input channels.
+        ``hid_channels`` (``int``): :math:`C_{hid}` is the number of hidden channels.
+        ``num_classes`` (``int``): The Number of class of the classification task.
+        ``use_bn`` (``bool``): If set to ``True``, use batch normalization. Defaults to ``False``.
+        ``drop_rate`` (``float``, optional): Dropout ratio. Defaults to 0.5.
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        hid_channels: int,
+        num_classes: int,
+        use_bn: bool = False,
+        drop_rate: float = 0.5,
+    ) -> None:
+        super().__init__()
+        self.layers = nn.ModuleList()
+        self.layers.append(
+            HGNNConv(in_channels, hid_channels, use_bn=use_bn, drop_rate=drop_rate)
+        )
+        self.layers.append(
+            HGNNConv(hid_channels, num_classes, use_bn=use_bn, is_last=True)
+        )
+
+    def forward(self, X: torch.Tensor, hg: "Hypergraph") -> torch.Tensor:
+        r"""The forward function.
+
+        Parameters:
+            ``X`` (``torch.Tensor``): Input vertex feature matrix. Size :math:`(N, C_{in})`.
+            ``hg`` (``eg.Hypergraph``): The hypergraph structure that contains :math:`N` vertices.
+        """
+
+        for layer in self.layers:
+            X = layer(X, hg)
+        return X