code: def convert_to_classifier(self):
      # Create victim_embedding placeholder
      self.victim_embedding_input = tf.placeholder(
          tf.float32, shape=(None, 512)
      )
  
      # Squared Euclidean Distance between embeddings
      distance = tf.reduce_sum(
          tf.square(
              self.embedding_output - self.victim_embedding_input
          ),
          axis=1,
      )
  
      # Convert distance to a softmax vector
      # 0.99 out of 4 is the distance threshold for the Facenet CNN
      threshold = 0.99
      score = tf.where(
          distance > threshold,
          0.5 + ((distance - threshold) * 0.5) / (4.0 - threshold),
          0.5 * distance / threshold,
      )
      reverse_score = 1.0 - score
      self.softmax_output = tf.transpose(
          tf.stack([reverse_score, score])
      )
  
      # Save softmax layer
      self.layer_names = []
      self.layers = []
      self.layers.append(self.softmax_output)
      self.layer_names.append("logits")
  
file_path: cleverhans/cleverhans_v3.1.0/examples/facenet_adversarial_faces/facenet_fgsm.py
imports: import facenet
  import tensorflow
  import numpy
  from cleverhans.model import Model
  from cleverhans.attacks import FastGradientMethod
  import set_loader
label_desc: Use image node_id 2 for calling the InceptionResnetV1Model obj's underlying member method code with example usage: obj.convert_to_classifier() without return types