docstr: :param x: Input data
  
  :return: output - mnist digit label for the input image
docstr_tok: [":","param","x",":","Input","data",":","return",":","output","-","mnist","digit","label","for","the","input","image"]
code: def forward(self, x):
      """
      :param x: Input data
  
      :return: output - mnist digit label for the input image
      """
      batch_size = x.size()[0]
  
      # (b, 1, 28, 28) -> (b, 1*28*28)
      x = x.view(batch_size, -1)
  
      # layer 1 (b, 1*28*28) -> (b, 128)
      x = self.layer_1(x)
      x = torch.relu(x)
  
      # layer 2 (b, 128) -> (b, 256)
      x = self.layer_2(x)
      x = torch.relu(x)
  
      # layer 3 (b, 256) -> (b, 10)
      x = self.layer_3(x)
  
      # probability distribution over labels
      x = torch.log_softmax(x, dim=1)
  
      return x
  
imports: import logging
  import os
  from argparse import ArgumentParser
  import mlflow
  import mlflow.pytorch
  import pytorch_lightning
  import torch
  from pytorch_lightning.callbacks import LearningRateMonitor
  from pytorch_lightning.callbacks import ModelCheckpoint
  from pytorch_lightning.callbacks.early_stopping import EarlyStopping
  from torch.nn import functional
  from torch.utils.data import DataLoader
  from torch.utils.data import random_split
  from torchvision import datasets
  from torchvision import transforms
label_desc: Use image node_id 3 for calling the LightningMNISTClassifier obj's underlying member method code with example usage: obj.forward(x) and returns: x