code: def forward(self, x):
      x = F.relu(F.max_pool2d(self.conv1(x), 3))
      x = x.view(-1, 192)
      x = self.fc(x)
      return F.log_softmax(x, dim=1)
  
file_path: ray/python/ray/tune/tests/tutorial.py
imports: import numpy
  import torch
  import torch.optim
  import torch.nn
  from torchvision import datasets, transforms
  from torch.utils.data import DataLoader
  import torch.nn.functional
  from ray import train, tune
  from ray.tune.schedulers import ASHAScheduler
  from hyperopt import hp
  from ray.tune.search.hyperopt import HyperOptSearch
  import os
  from ray.tune.examples.mnist_pytorch_trainable import TrainMNIST
label_desc: Use image node_id 2 for calling the ConvNet obj's underlying member method code with example usage: obj.forward(x) and returns: F