code: def call(self, prevprev, prev):
      prev_nodes_out = [self.preproc0(prevprev), self.preproc1(prev)]
      nodes_used_mask = tf.zeros(len(self.nodes) + 2, dtype=tf.bool)
      for i, node in enumerate(self.nodes):
          node_out, mask = node(prev_nodes_out)
          nodes_used_mask |= tf.pad(
              mask, [[0, nodes_used_mask.shape[0] - mask.shape[0]]]
          )
          prev_nodes_out.append(node_out)
  
      outputs = []
      for used, out, conv in zip(
          nodes_used_mask.numpy(), prev_nodes_out, self.conv_ops
      ):
          if not used:
              outputs.append(conv(out))
      out = tf.add_n(outputs)
      return prev, self.bn(out)
  
imports: import tensorflow
  from tensorflow.keras import Model, Sequential
  from tensorflow.keras.layers import AveragePooling2D, BatchNormalization, Conv2D, Dense, Dropout, GlobalAveragePooling2D, MaxPool2D, ReLU, SeparableConv2D
  from nni.nas.tensorflow.mutables import InputChoice, LayerChoice, MutableScope
label_desc: Use image node_id 2 for calling the ENASLayer obj's underlying member method code with example usage: obj.call(prevprev, prev) and returns: prev,  self