code: def call(self, user_id, p_item_id, n_item_id):
      user_vec = self.user_latent_factor(user_id)
      p_item_vec = self.item_latent_factor(p_item_id)
      p_item_bias = self.item_bias(p_item_id)
      n_item_vec = self.item_latent_factor(n_item_id)
      n_item_bias = self.item_bias(n_item_id)
  
      l2_user_pos = tf.math.reduce_sum(
          tf.math.square(user_vec - p_item_vec), axis=-1, keepdims=True
      )
      l2_user_neg = tf.math.reduce_sum(
          tf.math.square(user_vec - n_item_vec), axis=-1, keepdims=True
      )
      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(tf.maximum(self.margin - diff, 0))
      l2_loss = (
          tf.nn.l2_loss(user_vec)
          + tf.nn.l2_loss(p_item_vec)
          + tf.nn.l2_loss(n_item_vec)
      )
  
      return loss, l2_loss
  
file_path: openrec/openrec/tf2/recommenders/ucml.py
imports: import tensorflow
  from tensorflow.keras import Model
  from openrec.tf2.modules import LatentFactor
label_desc: Use image node_id 2 for calling the UCML obj's underlying member method code with example usage: obj.call(user_id, p_item_id, n_item_id) and returns: loss,  l2_loss