docstr: dense_features shape: [batch_size, num of dense features]
  sparse_features shape: [batch_size, num_of_sparse_features]
  label shape: [batch_size]
docstr_tok: ["dense_features","shape",":","[","batch_size",",","num","of","dense","features","]","sparse_features","shape",":","[","batch_size",",","num_of_sparse_features","]","label","shape",":","[","batch_size","]"]
code: def call(self, dense_features, sparse_features, label):
      """
      dense_features shape: [batch_size, num of dense features]
      sparse_features shape: [batch_size, num_of_sparse_features]
      label shape: [batch_size]
      """
  
      prediction = self.inference(dense_features, sparse_features)
      loss = self._loss(y_true=label, y_pred=prediction)
      return loss
  
file_path: openrec/openrec/tf2/recommenders/dlrm.py
imports: import sys
  import tensorflow
  from tensorflow.keras import Model
  from openrec.tf2.modules import LatentFactor, SecondOrderFeatureInteraction, MLP
label_desc: Use image node_id 2 for calling the DLRM obj's underlying member method code with example usage: obj.call(dense_features, sparse_features, label) and returns: loss