code: def test_custom_loss_graph(self):
      import tensorflow as tf
      import tensorrec
  
      # Define a custom loss graph
      class SimpleLossGraph(tensorrec.loss_graphs.AbstractLossGraph):
          def connect_loss_graph(
              self,
              tf_prediction_serial,
              tf_interactions_serial,
              **kwargs,
          ):
              """
              This loss function returns the absolute simple error between the predictions and the interactions.
              :param tf_prediction_serial: tf.Tensor
              The recommendation scores as a Tensor of shape [n_samples, 1]
              :param tf_interactions_serial: tf.Tensor
              The sample interactions corresponding to tf_prediction_serial as a Tensor of shape [n_samples, 1]
              :param kwargs:
              Other TensorFlow nodes.
              :return:
              A tf.Tensor containing the learning loss.
              """
              return tf.reduce_mean(
                  tf.abs(tf_interactions_serial - tf_prediction_serial)
              )
  
      # Build a model with the custom loss function
      model = tensorrec.TensorRec(loss_graph=SimpleLossGraph())
  
      # Generate some dummy data
      (
          interactions,
          user_features,
          item_features,
      ) = tensorrec.util.generate_dummy_data(
          num_users=100, num_items=150, interaction_density=0.05
      )
  
      # Fit the model for 5 epochs
      model.fit(
          interactions,
          user_features,
          item_features,
          epochs=5,
          verbose=True,
      )
  
      self.assertIsNotNone(model)
  
imports: from unittest import TestCase
label_desc: Use image node_id 3 for calling the ReadmeTestCase obj's underlying member method code with example usage: obj.test_custom_loss_graph() and returns: tf