code: def __init__(self, vocab_size: int):
      super().__init__()
      self.emb = nn.Embedding(vocab_size, 16)
      self.pool = nn.AdaptiveAvgPool1d(1)
      self.fc1 = nn.Linear(16, 16)
      self.fc2 = nn.Linear(16, 2)
  
imports: import argparse
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from datasets import load_dataset
  from opacus import PrivacyEngine
  from torch.nn.utils.rnn import pad_sequence
  from torch.utils.data import DataLoader
  from tqdm import tqdm
  from transformers import BertTokenizerFast
label_desc: Use image node_id 1 to create a new SampleNet object from inherited base classes: nn with example: obj = SampleNet(vocab_size)