class_docstr: A simple CNN module for MNIST, similar to pytorch's MNIST example.
  
  See: https://github.com/pytorch/examples/blob/master/mnist/main.py
class_docstr_tok: ["A","simple","CNN","module","for","MNIST",",","similar","to","pytorch","'s","MNIST","example",".","See",":","https",":","\/\/github.com\/pytorch\/examples\/blob\/master\/mnist\/main.py"]
code: def __init__(self):
      super().__init__()
      self.conv1 = nn.Conv2d(1, 32, 3, 1)
      self.conv2 = nn.Conv2d(32, 64, 3, 1)
      self.dropout1 = nn.Dropout2d(0.25)
      self.dropout2 = nn.Dropout2d(0.5)
      self.fc1 = nn.Linear(9216, 128)
      self.fc2 = nn.Linear(128, 10)
  
imports: import comet_ml
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from torch.utils.data import DataLoader
  import torchvision
  from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator
  from ignite.metrics import Accuracy, Loss
  import numpy
  from loguru import logger
  from ..model_wrapper import ModelWrapper
  from .None import preprocessing, visualization
  from ..None import utils
label_desc: Use image node_id 1 to create a new SimpleCnn object from inherited base classes: nn with example: obj = SimpleCnn()