code: def __init__(self, num_classes: int, n=5, c=3):
      super().__init__()
  
      self.is_train = True
      input_dims = c
      output_dims = 16
  
      first_conv = Sequential(
          conv3x3(input_dims, output_dims, stride=(1, 1)),
          batch_norm(16),
          ReLU(True),
      )
  
      input_dims = output_dims
      output_dims = 16
  
      # first stack of residual blocks, output is 16 x 32 x 32
      layers_list = []
      for _ in range(n):
          layers_list.append(ResidualBlock(input_dims))
      first_block = Sequential(*layers_list)
  
      input_dims = output_dims
      output_dims = 32
  
      # second stack of residual blocks, output is 32 x 16 x 16
      layers_list = [ResidualBlock(input_dims, increase_dim=True)]
      for _ in range(1, n):
          layers_list.append(ResidualBlock(output_dims))
      second_block = Sequential(*layers_list)
  
      input_dims = output_dims
      output_dims = 64
  
      # third stack of residual blocks, output is 64 x 8 x 8
      layers_list = [ResidualBlock(input_dims, increase_dim=True)]
      for _ in range(1, n - 1):
          layers_list.append(ResidualBlock(output_dims))
      layers_list.append(ResidualBlock(output_dims, last=True))
      third_block = Sequential(*layers_list)
      final_pool = AdaptiveAvgPool2d(output_size=(1, 1))
  
      self.feature_extractor = Sequential(
          first_conv,
          first_block,
          second_block,
          third_block,
          final_pool,
          Flatten(),
      )
  
      input_dims = output_dims
      output_dims = num_classes
  
      self.classifier = Linear(input_dims, output_dims)
  
imports: from typing import Union, Sequence, Callable
  import torch
  from torch.nn import Module, Sequential, BatchNorm2d, Conv2d, ReLU, ConstantPad3d, Identity, AdaptiveAvgPool2d, Linear
  from torch import Tensor
  from torch.nn.init import zeros_, kaiming_normal_
  from torch.nn.modules.flatten import Flatten
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new IcarlNet object from inherited base classes: Module with example: obj = IcarlNet(num_classes, n, c)