code: def inference(self, user_id):
      user_vec = self.user_latent_factor(user_id)
      return tf.linalg.matmul(
          user_vec,
          self.item_latent_factor.variables[0],
          transpose_b=True,
      ) + tf.reshape(self.item_bias.variables[0], [-1])
  
file_path: openrec/openrec/tf2/recommenders/wrmf.py
imports: import tensorflow
  from tensorflow.keras import Model
  from openrec.tf2.modules import LatentFactor, PointwiseMSELoss
label_desc: Use image node_id 3 for calling the WRMF obj's underlying member method code with example usage: obj.inference(user_id) and returns: unknown