code: def forward(self, x):
      x = self.pool(F.relu(self.conv1(x)))
      x = self.pool(F.relu(self.conv2(x)))
      x = x.view(-1, 16 * 6 * 6)
      x = F.relu(self.fc1(x))
      x = F.relu(self.fc2(x))
      x = self.fc3(self.dorpout(x))
      return x
  
imports: from pathlib import Path
  import matplotlib.pyplot
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  import torchvision.datasets
  import torchvision.transforms
  from ignite.contrib.handlers import TensorboardLogger
  from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator
  from ignite.handlers import global_step_from_engine
  from ignite.metrics import Accuracy, Loss, Recall
  from ignite.utils import setup_logger
  from torch.utils.tensorboard import SummaryWriter
  from tqdm import tqdm
  from clearml import Task, StorageManager
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: x