code: def forward(self, x):
      x = F.relu(F.max_pool2d(self.conv1(x), 2))
      x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
      x = x.view(-1, 320)
      x = F.relu(self.fc1(x))
      x = F.dropout(x, training=self.training)
      x = self.fc2(x)
      return F.log_softmax(x, dim=-1)
  
imports: import json
  import os
  import pickle
  from collections import Counter, OrderedDict
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from gensim.models import FastText
  from nltk.tokenize import sent_tokenize, word_tokenize
  from scipy.linalg import eig
  from skimage.filters import threshold_yen
label_desc: Use image node_id 2 for calling the MNIST_Classifier obj's underlying member method code with example usage: obj.forward(x) and returns: F