code: def __init__(self):
      super().__init__()
      self.conv1 = nn.Conv2d(1, 16, 8, 3)
      self.conv2 = nn.Conv1d(16, 32, 3, 1)
      # fully frozen layer
      self.convf = nn.Conv1d(32, 32, 1, 1)
      for p in self.convf.parameters():
          p.requires_grad = False
      # partially frozen layer
      self.convpf = nn.Conv1d(32, 32, 1, 1)
      self.convpf.weight.requires_grad = False
      self.fc1 = nn.Linear(23, 17)
      self.fc2 = nn.Linear(32 * 17, 10)
  
imports: import unittest
  from typing import Iterable, Optional
  import torch
  import torch.nn
  import torch.nn.functional
  from opacus import PrivacyEngine
  from opacus.grad_sample.utils import get_gsm_class
  from opacus.utils.batch_memory_manager import BatchMemoryManager
  from torch.utils.data import DataLoader
  from torchvision import transforms
  from torchvision.datasets import FakeData
label_desc: Use image node_id 1 to create a new SampleConvNet object from inherited base classes: nn with example: obj = SampleConvNet()