class_docstr: Tests the default trainer on single device setup.
class_docstr_tok: ["Tests","the","default","trainer","on","single","device","setup","."]
docstr: Given the train_state, trains the model on fake batches.
docstr_tok: ["Given","the","train_state",",","trains","the","model","on","fake","batches","."]
code: def train_and_evaluation(
      self, model, train_state, fake_batches, metrics_fn
  ):
      """Given the train_state, trains the model on fake batches."""
      eval_metrics = []
      fake_batches_replicated = jax_utils.replicate(fake_batches)
  
      eval_step_pmapped = jax.pmap(
          functools.partial(
              classification_trainer.eval_step,
              flax_model=model,
              metrics_fn=metrics_fn,
              debug=False,
          ),
          axis_name="batch",
          donate_argnums=(1,),
      )
      for fake_batch in fake_batches_replicated:
          metrics, _ = eval_step_pmapped(train_state, fake_batch)
          metrics = train_utils.unreplicate_and_get(metrics)
          eval_metrics.append(metrics)
      eval_metrics = train_utils.stack_forest(eval_metrics)
      eval_summary = jax.tree_util.tree_map(
          lambda x: x.sum(), eval_metrics
      )
      for key, val in eval_summary.items():
          eval_summary[key] = val[0] / val[1]
      return eval_summary
  
imports: import functools
  import shutil
  import tempfile
  from absl.testing import absltest
  from clu import metric_writers
  import flax
  from flax import jax_utils
  import flax.linen
  import jax.numpy
  import jax.random
  import ml_collections
  import numpy
  from scenic.dataset_lib import datasets
  from scenic.model_lib import models
  from scenic.model_lib.base_models import classification_model
  from scenic.model_lib.base_models import multilabel_classification_model
  from scenic.train_lib import classification_trainer
  from scenic.train_lib import lr_schedules
  from scenic.train_lib import optimizers
  from scenic.train_lib import train_utils
  import tensorflow
  import tensorflow_datasets
label_desc: Use image node_id 4 for calling the ClassificationTrainerTest obj's underlying member method code with example usage: obj.train_and_evaluation(model, train_state, fake_batches, metrics_fn) and returns: eval_summary