code: def similarity_cost(inputs_encoded, targets_encoded):
      """Loss telling to be more similar to your own targets than to others."""
      # This is a first very simple version: handle variable-length by padding
      # to same length and putting everything into batch. In need of a better way.
      x, y = common_layers.pad_to_same_length(
          inputs_encoded, targets_encoded
      )
      depth = tf.shape(inputs_encoded)[3]
      x, y = tf.reshape(x, [-1, depth]), tf.reshape(y, [-1, depth])
      return rank_loss(x, y)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from six.moves import range
  from six.moves import zip
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import common_hparams
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import modalities
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
label_desc: Use image node_id 4 for calling a global function with example usage: similarity_cost(inputs_encoded, targets_encoded) and returns: rank_loss