code: def __init__(self, args, images):
      super(CNN, self).__init__()
  
      self.args = args
      self.images = images
  
      self.w_c, self.w_h = defaultdict(dict), defaultdict(dict)
      self.reset_parameters()
  
      self.conv = defaultdict(dict)
      for idx in range(args.num_blocks):
          for jdx in range(idx + 1, args.num_blocks):
              self.conv[idx][jdx] = conv()
  
      raise NotImplemented("In progress...")
  
file_path: ENAS-pytorch/models/shared_cnn.py
imports: import numpy
  from collections import defaultdict, deque
  import torch
  from torch import nn
  import torch.nn.functional
  from torch.autograd import Variable
  from models.shared_base import *
  from utils import get_logger, get_variable, keydefaultdict
label_desc: Use image node_id 1 to create a new CNN object from inherited base classes: SharedModel with example: obj = CNN(args, images)