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 = self.conv1(g, x, edge_weight=edge_weight)
      x = function.leaky_relu(x, negative_slope=0.01)
      x = function.dropout(x, p=0.2, training=self.training)
  
      x = self.conv2(g, x, edge_weight=edge_weight)
      x = function.leaky_relu(x, negative_slope=0.01)
      x = function.dropout(x, p=0.2, training=self.training)
  
      x = self.conv3(g, x, edge_weight=edge_weight)
      x = function.leaky_relu(x, negative_slope=0.01)
      x = function.dropout(x, p=0.2, training=self.training)
  
      x = self.conv4(g, x, edge_weight=edge_weight)
      x = self.conv4_bn(x)
      x = function.leaky_relu(x, negative_slope=0.01)
      x = function.dropout(x, p=0.2, training=self.training)
      # NOTE: this takes node-level features/"embeddings"
      # and aggregates to graph-level - use for graph-level classification
  
      out = self.sumpool(g, x)
      if return_graph_embedding:
          return out
  
      out = function.leaky_relu(self.fc_block1(out), negative_slope=0.1)
      out = function.dropout(out, p=0.2, training=self.training)
  
      out = function.leaky_relu(self.fc_block2(out), negative_slope=0.1)
      out = function.dropout(out, p=0.2, training=self.training)
  
      out = self.fc_block3(out)
      return out
  
file_path: dgl/examples/pytorch/eeg-gcnn/deep_EEGGraphConvNet.py
imports: import torch.nn
  import torch.nn.functional
  from dgl.nn import GraphConv, SumPooling
  from torch.nn import BatchNorm1d
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