class_docstr: EEGGraph Convolution Net
  Parameters
  ----------
  num_feats: the number of features per node. In our case, it is 6.
code: def forward(self, g, return_graph_embedding=False):
      x = g.ndata["x"]
      edge_weight = g.edata["edge_weights"]
  
      x = function.leaky_relu(self.conv1(g, x, edge_weight=edge_weight))
      x = function.leaky_relu(
          self.conv2_bn(self.conv2(g, x, edge_weight=edge_weight))
      )
  
      # NOTE: this takes node-level features/"embeddings"
      # and aggregates to graph-level - use for graph-level classification
      sumpool = SumPooling()
      out = sumpool(g, x)
      if return_graph_embedding:
          return out
  
      out = function.dropout(out, p=0.2, training=self.training)
      out = self.fc_block1(out)
      out = function.leaky_relu(out)
      out = self.fc_block2(out)
  
      return out
  
imports: import torch.nn
  import torch.nn.functional
  from dgl.nn import GraphConv, SumPooling
label_desc: Use image node_id 2 for calling the EEGGraphConvNet obj's underlying member method code with example usage: obj.forward(g, return_graph_embedding) and returns: out, out