code: def forward(self, x):
      x = F.relu(self.bn1(self.conv1(x)))
      x = F.max_pool2d(x, 2, 2)
      x = F.relu(self.bn2(self.conv2(x)))
      x = F.max_pool2d(x, 2, 2)
      x = x.view(-1, 4 * 4 * 10)
      x = F.relu(self.fc1(x))
      x = self.fc2(x)
      return F.log_softmax(x, dim=1)
  
imports: from unittest import TestCase, main
  import numpy
  import torch
  import torch.nn.functional
  import schema
  from aup.compression.torch.pruning import *
  from aup.compression.torch.quantization import *
  import math
label_desc: Use image node_id 2 for calling the TorchModel obj's underlying member method code with example usage: obj.forward(x) and returns: F