code: def triplet_loss(
      anchor,
      positive,
      negative,
      margin,
      extra=False,
      scope="triplet_loss",
  ):
      r"""Loss for Triplet networks as described in the paper:
      `FaceNet: A Unified Embedding for Face Recognition and Clustering
      <https://arxiv.org/abs/1503.03832>`_
      by Schroff et al.
  
      Learn embeddings from an anchor point and a similar input (positive) as
      well as a not-similar input (negative).
      Intuitively, a matching pair (anchor, positive) should have a smaller relative distance
      than a non-matching pair (anchor, negative).
  
      .. math::
          \max(0, m + \Vert a-p\Vert^2 - \Vert a-n\Vert^2)
  
      Args:
          anchor (tf.Tensor): anchor feature vectors of shape [Batch, N].
          positive (tf.Tensor): features of positive match of the same shape.
          negative (tf.Tensor): features of negative match of the same shape.
          margin (float): horizon for negative examples
          extra (bool): also return distances for pos and neg.
  
      Returns:
          tf.Tensor: triplet-loss as scalar (and optionally average_pos_dist, average_neg_dist)
      """
  
      with tf.name_scope(scope):
          d_pos = tf.reduce_sum(tf.square(anchor - positive), 1)
          d_neg = tf.reduce_sum(tf.square(anchor - negative), 1)
  
          loss = tf.reduce_mean(tf.maximum(0.0, margin + d_pos - d_neg))
  
          if extra:
              pos_dist = tf.reduce_mean(
                  tf.sqrt(d_pos + 1e-10), name="pos-dist"
              )
              neg_dist = tf.reduce_mean(
                  tf.sqrt(d_neg + 1e-10), name="neg-dist"
              )
              return loss, pos_dist, neg_dist
          else:
              return loss
  
imports: import argparse
  import numpy
  import tensorflow
  import tensorflow.contrib.slim
  from tensorpack import *
  from tensorpack.tfutils.summary import add_moving_summary
  from tensorpack.utils.gpu import change_gpu
  from embedding_data import MnistPairs, MnistTriplets, get_test_data
label_desc: Use image node_id 3 for calling a global function with example usage: triplet_loss(anchor, positive, negative, margin, extra, scope) and returns: loss,  pos_dist,  neg_dist, loss