code: def forward(self, x):
      batch_size, channels, width, height = x.size()
      x = x.view(batch_size, -1)
      x = self.layer_1(x)
      x = torch.relu(x)
      x = self.layer_2(x)
      x = torch.relu(x)
      x = self.layer_3(x)
      x = torch.log_softmax(x, dim=1)
      return x
  
imports: import math
  import os
  import torch
  from filelock import FileLock
  import pytorch_lightning
  from torch.nn import functional
  from torch.utils.data import DataLoader, random_split
  from torchmetrics import Accuracy
  from torchvision import transforms
  from torchvision.datasets import MNIST
  from ray.tune.integration.pytorch_lightning import TuneReportCheckpointCallback
  from ray import train, tune
label_desc: Use image node_id 2 for calling the LightningMNISTClassifier obj's underlying member method code with example usage: obj.forward(x) and returns: x