code: def __init__(self):
      super(Net, self).__init__()
      self.conv1 = nn.Conv2d(3, 6, 3)
      self.conv2 = nn.Conv2d(6, 16, 3)
      self.pool = nn.MaxPool2d(2, 2)
      self.fc1 = nn.Linear(16 * 6 * 6, 120)
      self.fc2 = nn.Linear(120, 84)
      self.dorpout = nn.Dropout(p=params.get("dropout", 0.25))
      self.fc3 = nn.Linear(84, 10)
  
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 1 to create a new Net object from inherited base classes: nn with example: obj = Net()