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 forward(self, x):
      x = self.conv1(x)
      x = F.relu(x)
      x = self.conv2(x)
      x = F.relu(x)
      x = F.max_pool2d(x, 2)
      x = self.dropout1(x)
      x = torch.flatten(x, 1)
      x = self.fc1(x)
      x = F.relu(x)
      x = self.dropout2(x)
      x = self.fc2(x)
      output = F.log_softmax(x, dim=1)
      return output
  
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 2 for calling the SimpleCnn obj's underlying member method code with example usage: obj.forward(x) and returns: output