code: def forward(self, x):
      x = F.relu(F.max_pool2d(self.conv1(x), 2))
      x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
      x = x.view(-1, 320)
      x = F.relu(self.fc1(x))
      x = F.dropout(x, training=self.training)
      x = self.fc2(x)
      return F.log_softmax(x, dim=-1)
  
file_path: fastai/nbs/examples/migrating_ignite.py
imports: from fastai.data.load import DataLoader
  import torch
  from torch import nn
  from torch.optim import SGD
  import torch.nn.functional
  from torchvision.transforms import Compose, ToTensor, Normalize
  from torchvision.datasets import MNIST
  from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator
  from ignite.metrics import Accuracy, Loss
  from ignite.utils import setup_logger
label_desc: Use image node_id 2 for calling the Net obj's underlying member method code with example usage: obj.forward(x) and returns: F