Loss is getting clamped in my GCN model [P]

I have trained the following model using pytorch on graphs having the same edge index(task is graph classification on Electronic health records where each graph represents the patients data and node vectors have been derived form a combined knowledge graph)

class mdl(torch.nn.Module):
def init(self, input_size, hidden_size, output_size,dropout_rate):
super(GCNClassifier, self).init()
self.conv1 = GCNConv(input_size, hidden_size)
self.conv2 = GCNConv(hidden_size, output_size)
self.dropout = torch.nn.Dropout(dropout_rate)


def forward(self, x, edge_index):
x = self.conv1(x, edge_index)
x = F.relu(x)
x = self.dropout(x)
x = self.conv2(x, edge_index)
x = …

class classification data edge electronic electronic health records form graph graphs health index knowledge knowledge graph loss machinelearning node patients pytorch records torch vectors