Thank you for the suggestion. I tried:
class GCNN(torch.nn.Module):
    def __init__(self):
        super(GCNN, self).__init__()
        self.conv1 = GCNConv(new_dataset[0].num_node_features, 957)
        self.conv2 = GCNConv(957, 480)
        self.conv3 = GCNConv(480, new_dataset[0].num_classes)

    def forward(self, data):
        x, edge_index = data.x, data.edge_index

        x = self.conv1(x, edge_index)
        x = F.relu(x)
        x = F.dropout(x, training=self.training)
        x = self.conv2(x, edge_index)
        x = F.relu(x)
        x = F.dropout(x, training=self.training)
        x = self.conv3(x, edge_index)
        x = F.relu(x)

        return F.log_softmax(x, dim=1)


and
class GCNN(torch.nn.Module):
    def __init__(self):
        super(GCNN, self).__init__()
        self.conv1 = GCNConv(new_dataset[0].num_node_features, 957)
        self.conv2 = GCNConv(957, 480)
        self.conv3 = GCNConv(480, new_dataset[0].num_classes)
        self.dropout1 = nn.Dropout(0.25)
        self.dropout2 = nn.Dropout(0.25)

    def forward(self, data):
        x, edge_index = data.x, data.edge_index

        x = F.relu(self.conv1(x, edge_index))
        x = self.dropout1(x)
        x = F.relu(self.conv2(x, edge_index))
        x = self.dropout2(x)
        x = F.relu(self.conv3(x, edge_index))

        return F.log_softmax(x, dim=1)

and
class GCNN(torch.nn.Module):
    def __init__(self):
        super(GCNN, self).__init__()
        self.conv1 = GCNConv(new_dataset[0].num_node_features, 957)
        self.conv2 = GCNConv(957, 480)
        self.conv3 = GCNConv(480, new_dataset[0].num_classes)
        self.dropout1 = nn.Dropout(0.25)
        self.dropout2 = nn.Dropout(0.25)
        self.relu1 = nn.ReLU()
        self.relu2 = nn.ReLU()
        self.relu3 = nn.ReLU()

    def forward(self, data):
        x, edge_index = data.x, data.edge_index

        x = self.conv1(x, edge_index)
        x = self.relu1(x)
        x = self.dropout1(x)
        x = self.conv2(x, edge_index)
        x = self.relu2(x)
        x = self.dropout2(x)
        x = self.conv3(x, edge_index)
        x = self.relu3(x)

        return F.log_softmax(x, dim=1)


Unfortunately, none of these options worked.