class_docstr: Evaluates serialized sequence prediction models.
  
  Example: time series prediction. We can serialize a time series into
  a sequence of discrete tokens and model this sequence using an autoregressive
  sequence model, such as Transformer - see
  `trax.rl.serialization_utils.SerializedModel`. Then we can use this callback
  to evaluate long-horizon predictions of such a model.
code: def on_step_end(self, step):
      summary_writer = jaxboard.SummaryWriter(
          os.path.join(self._loop.output_dir, "srl_eval")
      )
      try:
          weights = self._loop.eval_model.seq_model_weights
          metrics = self.evaluate(weights)
          self._loop.log_summary(
              metrics, summary_writer, "", "srl_eval"
          )
      finally:
          summary_writer.close()
  
imports: import collections
  import os
  import gin
  import numpy
  from trax import jaxboard
  from trax import layers
  from trax import shapes
  from trax.rl import serialization_utils
  from trax.supervised import decoding
label_desc: Use image node_id 5 for calling the SerializedModelEvaluation obj's underlying member method code with example usage: obj.on_step_end(step) without return types