code: def call(self, inputs):
      user_vec, item_vec, item_bias, label = inputs
  
      dot_user_item = tf.math.reduce_sum(
          tf.math.multiply(user_vec, item_vec),
          axis=1,
          keepdims=False,
          name="dot_user_item",
      )
  
      if self._sigmoid:
          prediction = tf.math.sigmoid(
              dot_user_item + tf.reshape(item_bias, [-1])
          )
      else:
          prediction = dot_user_item + tf.reshape(item_bias, [-1])
  
      label_weight = (self._a - self._b) * label + self._b
      return tf.math.reduce_sum(
          label_weight * tf.square(label - prediction)
      )
  
imports: import tensorflow
  from tensorflow.keras.layers import Layer
label_desc: Use image node_id 3 for calling the PointwiseMSELoss obj's underlying member method code with example usage: obj.call(inputs) and returns: tf