code: def loss_fn(self, pos_scores, neg_scores):
      pos_scores = tf.reshape(pos_scores, [-1, 1, 1])
      if self.corrupt == "both":
          neg_scores_mean = tf.reduce_mean(
              tf.reshape(neg_scores, [-1, 2 * self.num_negs]),
              axis=-1,
              keep_dims=True,
          )
      else:
          neg_scores_mean = tf.reduce_mean(
              tf.reshape(neg_scores, [-1, self.num_negs]),
              axis=-1,
              keep_dims=True,
          )
      neg_scores_mean = tf.reshape(neg_scores_mean, [-1, 1, 1])
      loss = tf.reduce_mean(
          tf.maximum(self.margin + neg_scores_mean - pos_scores, 0)
      )
      return loss
  
file_path: euler/examples/TransX/transH.py
imports: from transX import TransX
  import tensorflow
  import tf_euler
label_desc: Use image node_id 4 for calling the TransH obj's underlying member method code with example usage: obj.loss_fn(pos_scores, neg_scores) and returns: loss