class_docstr: A helper class to build the summary over the training iterations.
class_docstr_tok: ["A","helper","class","to","build","the","summary","over","the","training","iterations","."]
docstr: Update with the given per-step metrics.
docstr_tok: ["Update","with","the","given","per-step","metrics","."]
code: def update(self, metrics_update, extra_logs_update):
      """Update with the given per-step metrics."""
      self.metrics.append(metrics_update)
      self.extra_logs.append(extra_logs_update)
  
imports: import copy
  import dataclasses
  import functools
  import json
  import os
  import random
  from typing import Any, Callable, Dict, List, Optional, Tuple
  from absl import logging
  from clu import metric_writers
  from clu import periodic_actions
  from dmvr import tokenizers
  from flax import jax_utils
  from flax.core import unfreeze
  import flax.linen
  import jax
  import jax.example_libraries.optimizers
  import jax.numpy
  import jax.profiler
  import ml_collections
  import numpy
  from scenic.dataset_lib import dataset_utils
  from scenic.model_lib.base_models import base_model
  from scenic.projects.t5 import model
  from scenic.projects.t5 import tokenizer
  from scenic.projects.vid2seq import load_utils
  from scenic.projects.vid2seq import models
  from scenic.projects.vid2seq import train_utils
  from scenic.projects.vid2seq import dvc_eval
  from scenic.train_lib_deprecated import lr_schedules
  from scenic.train_lib_deprecated import optimizers
  from scenic.train_lib_deprecated import pretrain_utils
  from scenic.train_lib_deprecated import train_utils
label_desc: Use image node_id 1 for calling the SummaryBuilder obj's underlying member method code with example usage: obj.update(metrics_update, extra_logs_update) without return types