code: def __init__(self, C_in, C_out, affine=False):
      super().__init__()
      self.conv1 = nn.Conv2d(
          C_in, C_out // 2, 1, stride=2, padding=0, bias=False
      )
      self.conv2 = nn.Conv2d(
          C_in, C_out // 2, 1, stride=2, padding=0, bias=False
      )
      self.bn = nn.BatchNorm2d(C_out, affine=affine)
  
file_path: nni/examples/nas/legacy/search_space_zoo/enas_macro_example.py
imports: import torch
  import logging
  import torch.nn
  import torch.nn.functional
  from argparse import ArgumentParser
  from torchvision import transforms
  from torchvision.datasets import CIFAR10
  from nni.nas.pytorch import mutables
  from nni.algorithms.nas.pytorch import enas
  from utils import accuracy, reward_accuracy
  from nni.nas.pytorch.callbacks import ArchitectureCheckpoint, LRSchedulerCallback
  from nni.nas.pytorch.search_space_zoo import ENASMacroLayer
  from nni.nas.pytorch.search_space_zoo import ENASMacroGeneralModel
label_desc: Use image node_id 1 to create a new FactorizedReduce object from inherited base classes: nn with example: obj = FactorizedReduce(C_in, C_out, affine)