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/doc/source/tune/doc_code/pytorch_optuna.py
imports: import os
  from filelock import FileLock
  import torch.nn
  import torch.nn.functional
  from torchvision import datasets, transforms
  import torch
  from ray import train, tune
  from ray.tune.search.optuna import OptunaSearch
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