code: def train_step(self, data):
      x, y, sample_weight = self.unpack_training_data(data)
      with tf.GradientTape() as tape:
          (
              (potentials, sequence_length, kernel),
              decoded_sequence,
              *_,
          ) = self(x, training=True, 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)
      gradients = tape.gradient(loss, self.trainable_variables)
      self.optimizer.apply_gradients(
          zip(gradients, self.trainable_variables)
      )
  
      # Update metrics (includes the metric that tracks the loss)
      self.compiled_metrics.update_state(y, decoded_sequence)
      # Return a dict mapping metric names to current value
      orig_results = {m.name: m.result() for m in self.metrics}
      crf_results = {"loss": loss, "crf_loss": crf_loss}
      return {**orig_results, **crf_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 5 for calling the CRFModelWrapper obj's underlying member method code with example usage: obj.train_step(data) and returns: dict