code: def PairwiseEuDist(
      user_vec,
      subgraph,
      item_vec=None,
      item_bias=None,
      p_item_vec=None,
      p_item_bias=None,
      n_item_vec=None,
      n_item_bias=None,
      train=True,
      weights=1.0,
      margin=1.0,
      scope="PointwiseMSE",
  ):
      if train:
          l2_user_pos = tf.reduce_sum(
              tf.square(tf.subtract(user_vec, p_item_vec)),
              reduction_indices=1,
              keepdims=True,
              name="l2_user_pos",
          )
          l2_user_neg = tf.reduce_sum(
              tf.square(tf.subtract(user_vec, n_item_vec)),
              reduction_indices=1,
              keepdims=True,
              name="l2_user_neg",
          )
          pos_score = (-l2_user_pos) + p_item_bias
          neg_score = (-l2_user_neg) + n_item_bias
          diff = pos_score - neg_score
          loss = tf.reduce_sum(weights * tf.maximum(margin - diff, 0))
          subgraph.register_global_loss(loss)
      else:
          predictions = (
              -tf.reduce_sum(
                  tf.square(tf.subtract(user_vec, item_vec)),
                  reduction_indices=1,
                  keepdims=True,
                  name="l2_user_pos",
              )
              + item_bias
          )
          subgraph.register_global_output(predictions)
  
label_desc: Use image node_id 1 for calling a global function with example usage: PairwiseEuDist(user_vec, subgraph, item_vec, item_bias, p_item_vec, p_item_bias, n_item_vec, n_item_bias, train, weights, margin, scope) without return types