code: def test_step(self, data):
      x, y, sample_weight = self.unpack_training_data(data)
      (potentials, sequence_length, kernel), decode_sequence, *_ = self(
          x, training=False, return_crf_internal=True
      )
      crf_loss = self.compute_crf_loss(
          potentials, sequence_length, kernel, y, sample_weight
      )
      loss = crf_loss + tf.reduce_sum(self.losses)
      # Update metrics (includes the metric that tracks the loss)
      self.compiled_metrics.update_state(y, decode_sequence)
      # Return a dict mapping metric names to current value
      results = {m.name: m.result() for m in self.metrics}
      results.update(
          {"loss": loss, "crf_loss": crf_loss}
      )  # append loss
      return results
  
file_path: addons/tensorflow_addons/text/crf_wrapper.py
imports: import tensorflow
  from tensorflow_addons.text import crf_log_likelihood
  from tensorflow_addons.utils import types
label_desc: Use image node_id 6 for calling the CRFModelWrapper obj's underlying member method code with example usage: obj.test_step(data) and returns: results