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.
docstr: Evaluates the model and returns the metrics.
docstr_tok: ["Evaluates","the","model","and","returns","the","metrics","."]
code: def evaluate(self, weights):
      """Evaluates the model and returns the metrics."""
      self._predict_model.weights = weights
  
      metrics = collections.defaultdict(list)
      for _ in range(self._n_steps):
          batch = self._eval_task.next_batch()
          step_metrics = self._evaluate_batch(batch)
          for key, value in step_metrics.items():
              metrics[key].append(value)
  
      metrics = {k: np.array(v) for (k, v) in metrics.items()}
  
      def metric_name(context, horizon):
          return f"pred_error/context_{context}/horizon_{horizon}"
  
      return {
          metric_name(context, horizon): np.sum(errors)
          / (np.sum(errors != 0) + 1e-6)
          for ((context, horizon), errors) in metrics.items()
      }
  
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 6 for calling the SerializedModelEvaluation obj's underlying member method code with example usage: obj.evaluate(weights) and returns: unknown, str, context, str, horizon