--- a
+++ b/kgwas/conv.py
@@ -0,0 +1,232 @@
+from typing import Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+from torch import Tensor
+from torch.nn import Parameter
+from torch_sparse import SparseTensor, set_diag
+
+from torch_geometric.nn.conv import MessagePassing
+from torch_geometric.nn.dense.linear import Linear
+from torch_geometric.typing import NoneType  # noqa
+from torch_geometric.typing import Adj, OptPairTensor, OptTensor, Size
+from torch_geometric.utils import add_self_loops, remove_self_loops, softmax
+from torch_geometric.nn.inits import glorot, zeros
+
+
+'''
+def group(xs: List[Tensor], aggr: Optional[str]) -> Optional[Tensor]:
+    if len(xs) == 0:
+        return None
+    elif aggr is None:
+        return torch.stack(xs, dim=1)
+    elif len(xs) == 1:
+        return xs[0]
+    elif isinstance(xs[0], tuple):
+        return xs
+    else:
+        out = torch.stack(xs, dim=0)
+        out = getattr(torch, aggr)(out, dim=0)
+        out = out[0] if isinstance(out, tuple) else out
+        return out
+'''
+
+
+
+class GATConv(MessagePassing):
+    def __init__(
+        self,
+        in_channels: Union[int, Tuple[int, int]],
+        out_channels: int,
+        heads: int = 1,
+        concat: bool = True,
+        negative_slope: float = 0.2,
+        dropout: float = 0.0,
+        add_self_loops: bool = True,
+        edge_dim: Optional[int] = None,
+        fill_value: Union[float, Tensor, str] = 'mean',
+        bias: bool = True,
+        sigmoid_gat: bool = False,
+        temperature: float = 1,
+        pheno_condition: bool = False,
+        **kwargs,
+    ):
+        kwargs.setdefault('aggr', 'add')
+        super().__init__(node_dim=0, **kwargs)
+
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.heads = heads
+        self.concat = concat
+        self.negative_slope = negative_slope
+        self.dropout = dropout
+        self.add_self_loops = add_self_loops
+        self.edge_dim = edge_dim
+        self.fill_value = fill_value
+        self.sigmoid_gat = sigmoid_gat
+        self.temperature = temperature
+        self.pheno_condition = pheno_condition
+        
+        if self.pheno_condition == 'ATT':
+            self.lin_edge_ = Linear(self.out_channels, heads * out_channels, bias=False,
+                                   weight_initializer='glorot')
+            self.att_edge = Parameter(torch.Tensor(1, heads, out_channels))
+            
+        elif self.pheno_condition == 'MSG':
+            self.pheno_mlp = Linear(edge_dim, heads * out_channels, bias=False,
+                                   weight_initializer='glorot')
+        
+        # In case we are operating in bipartite graphs, we apply separate
+        # transformations 'lin_src' and 'lin_dst' to source and target nodes:
+        if isinstance(in_channels, int):
+            self.lin_src = Linear(in_channels, heads * out_channels,
+                                  bias=False, weight_initializer='glorot')
+            self.lin_dst = self.lin_src
+        else:
+            self.lin_src = Linear(in_channels[0], heads * out_channels, False,
+                                  weight_initializer='glorot')
+            self.lin_dst = Linear(in_channels[1], heads * out_channels, False,
+                                  weight_initializer='glorot')
+
+        # The learnable parameters to compute attention coefficients:
+        self.att_src = Parameter(torch.Tensor(1, heads, out_channels))
+        self.att_dst = Parameter(torch.Tensor(1, heads, out_channels))
+
+        if edge_dim is not None:
+            self.lin_edge = Linear(edge_dim, heads * out_channels, bias=False,
+                                   weight_initializer='glorot')
+            self.att_edge = Parameter(torch.Tensor(1, heads, out_channels))
+        else:
+            self.lin_edge = None
+            self.register_parameter('att_edge', None)
+
+        if bias and concat:
+            self.bias = Parameter(torch.Tensor(heads * out_channels))
+        elif bias and not concat:
+            self.bias = Parameter(torch.Tensor(out_channels))
+        else:
+            self.register_parameter('bias', None)
+
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        self.lin_src.reset_parameters()
+        self.lin_dst.reset_parameters()
+        if self.lin_edge is not None:
+            self.lin_edge.reset_parameters()
+        glorot(self.att_src)
+        glorot(self.att_dst)
+        glorot(self.att_edge)
+        zeros(self.bias)
+
+    def forward(self, x: Union[Tensor, OptPairTensor], edge_index: Adj,
+                edge_attr: OptTensor = None, size: Size = None, pheno_emb = None,
+                return_attention_weights=None, return_raw_attention_weights = None):
+       
+        
+        if return_raw_attention_weights:
+            self.return_raw_attention_weights = True
+        else:
+            self.return_raw_attention_weights = False
+            
+        H, C = self.heads, self.out_channels
+
+        # We first transform the input node features. If a tuple is passed, we
+        # transform source and target node features via separate weights:
+        if isinstance(x, Tensor):
+            assert x.dim() == 2, "Static graphs not supported in 'GATConv'"
+            x_src = x_dst = self.lin_src(x).view(-1, H, C)
+        else:  # Tuple of source and target node features:
+            x_src, x_dst = x
+            assert x_src.dim() == 2, "Static graphs not supported in 'GATConv'"
+            x_src = self.lin_src(x_src).view(-1, H, C)
+            if x_dst is not None:
+                x_dst = self.lin_dst(x_dst).view(-1, H, C)
+
+        x = (x_src, x_dst)
+
+        # Next, we compute node-level attention coefficients, both for source
+        # and target nodes (if present):
+        alpha_src = (x_src * self.att_src).sum(dim=-1)
+        alpha_dst = None if x_dst is None else (x_dst * self.att_dst).sum(-1)
+        alpha = (alpha_src, alpha_dst)
+
+        if self.add_self_loops:
+            if isinstance(edge_index, Tensor):
+                # We only want to add self-loops for nodes that appear both as
+                # source and target nodes:
+                num_nodes = x_src.size(0)
+                if x_dst is not None:
+                    num_nodes = min(num_nodes, x_dst.size(0))
+                num_nodes = min(size) if size is not None else num_nodes
+                edge_index, edge_attr = remove_self_loops(
+                    edge_index, edge_attr)
+                edge_index, edge_attr = add_self_loops(
+                    edge_index, edge_attr, fill_value=self.fill_value,
+                    num_nodes=num_nodes)
+            elif isinstance(edge_index, SparseTensor):
+                if self.edge_dim is None:
+                    edge_index = set_diag(edge_index)
+                else:
+                    raise NotImplementedError(
+                        "The usage of 'edge_attr' and 'add_self_loops' "
+                        "simultaneously is currently not yet supported for "
+                        "'edge_index' in a 'SparseTensor' form")
+
+        # edge_updater_type: (alpha: OptPairTensor, edge_attr: OptTensor)
+        alpha = self.edge_updater(edge_index, alpha=alpha, edge_attr=edge_attr)
+        
+        #if self.return_raw_attention_weights:
+        #    return 0, (edge_index, alpha)
+        # propagate_type: (x: OptPairTensor, alpha: Tensor)
+        out = self.propagate(edge_index, x=x, alpha=alpha, size=size)
+
+        if self.concat:
+            out = out.view(-1, self.heads * self.out_channels)
+        else:
+            out = out.mean(dim=1)
+
+        if self.bias is not None:
+            out = out + self.bias
+
+        if isinstance(return_attention_weights, bool):
+            if isinstance(edge_index, Tensor):
+                return out, (edge_index, alpha)
+            elif isinstance(edge_index, SparseTensor):
+                return out, edge_index.set_value(alpha, layout='coo')
+        else:
+            return out
+
+    def edge_update(self, alpha_j: Tensor, alpha_i: OptTensor,
+                    edge_attr: OptTensor, index: Tensor, ptr: OptTensor,
+                    size_i: Optional[int]) -> Tensor:
+        # Given edge-level attention coefficients for source and target nodes,
+        # we simply need to sum them up to "emulate" concatenation:
+        alpha = alpha_j if alpha_i is None else alpha_j + alpha_i
+
+        if edge_attr is not None and self.lin_edge is not None:
+            if edge_attr.dim() == 1:
+                edge_attr = edge_attr.view(-1, 1)
+            #print(edge_attr)
+            #print(edge_attr.shape)
+            edge_attr = self.lin_edge(edge_attr)
+            edge_attr = edge_attr.view(-1, self.heads, self.out_channels)
+            alpha_edge = (edge_attr * self.att_edge).sum(dim=-1)
+            alpha = alpha + alpha_edge
+
+        alpha = F.leaky_relu(alpha, self.negative_slope)        
+            
+        if self.sigmoid_gat:
+            alpha = torch.sigmoid(alpha/self.temperature)
+        else:
+            if not self.return_raw_attention_weights:
+                alpha = softmax(alpha/self.temperature, index, ptr, size_i)
+        alpha = F.dropout(alpha, p=self.dropout, training=self.training)
+        return alpha
+
+    def message(self, x_j: Tensor, alpha: Tensor) -> Tensor:
+        return alpha.unsqueeze(-1) * x_j
+
+    def __repr__(self) -> str:
+        return (f'{self.__class__.__name__}({self.in_channels}, '
+                f'{self.out_channels}, heads={self.heads})')