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,
      return_confusion_matrix=False,
  ):
      """Given the train_state, trains the model on fake batches."""
      eval_metrics = []
      fake_batches_replicated = jax_utils.replicate(fake_batches)
      if return_confusion_matrix:
          confusion_matrices = []
  
      eval_step_pmapped = jax.pmap(
          functools.partial(
              vivit_train_utils.eval_step,
              flax_model=model,
              metrics_fn=metrics_fn,
              return_logits_and_labels=False,
              return_confusion_matrix=return_confusion_matrix,
              debug=False,
          ),
          axis_name="batch",
          donate_argnums=(1,),
      )
      for fake_batch in fake_batches_replicated:
          metric_data = eval_step_pmapped(
              train_state=train_state, batch=fake_batch
          )
          if return_confusion_matrix:
              metrics, confusion_matrix = metric_data
              confusion_matrices.append(
                  vivit_train_utils.to_cpu(confusion_matrix)
              )
          else:
              metrics = metric_data
          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]
  
      if return_confusion_matrix:
          confusion_matrix_summary = (
              evaluation_lib.compute_confusion_matrix_metrics(
                  confusion_matrices, return_per_class_metrics=True
              )
          )
          return eval_summary, confusion_matrix_summary
      else:
          return eval_summary
  
imports: import functools
  import shutil
  import os
  from absl.testing import absltest
  from absl.testing import parameterized
  import flax
  from flax import jax_utils
  import flax.linen
  import jax.numpy
  import jax.random
  import ml_collections
  import numpy
  from scenic.model_lib.base_models import classification_model
  from scenic.model_lib.base_models import multilabel_classification_model
  from scenic.projects.vivit import evaluation_lib
  from scenic.projects.vivit import train_utils
  from scenic.train_lib_deprecated import optimizers
  from scenic.train_lib_deprecated import train_utils
  import tensorflow
label_desc: Use image node_id 4 for calling the ViViTClassificationTrainerTest obj's underlying member method code with example usage: obj.train_and_evaluation(model, train_state, fake_batches, metrics_fn, return_confusion_matrix) and returns: eval_summary,  confusion_matrix_summary, eval_summary