code: def eval_step(dense_features, sparse_features, label):
      pred = dlrm_model.inference(dense_features, sparse_features)
      auc.update_state(y_true=label, y_pred=pred)
  
file_path: openrec/tf2_examples/dlrm_criteo.py
imports: from tensorflow.data import Dataset
  from openrec.tf2.recommenders import DLRM
  from tensorflow.keras import optimizers
  from tqdm import tqdm
  import tensorflow
  import dataloader
label_desc: Use image node_id 2 for calling a global function with example usage: eval_step(dense_features, sparse_features, label) without return types