code: def train_and_eval(
      *,
      rng: jnp.ndarray,
      config: ml_collections.ConfigDict,
      model_cls: Any,
      dataset: dataset_utils.Dataset,
      workdir: str,
      writer: metric_writers.MetricWriter,
      kb_datasets: Dict[str, dataset_utils.Dataset],
  ) -> Tuple[train_utils.TrainState, Dict[str, Any], Dict[str, Any]]:
      """Main training loop lives in this function.
  
      Given the model class and dataset, it prepares the items needed to run the
      training, including the TrainState.
  
      Args:
        rng: Jax rng key.
        config: Configurations of the experiment.
        model_cls: Model class; A model has a flax_module, a loss_fn, and a
          metrics_fn associated with it.
        dataset: The dataset that has train_iter, eval_iter, meta_data, and
          optionally, test_iter.
        workdir: Directory for checkpointing.
        writer: CLU metrics writer instance.
        kb_datasets: dictionary of datasets served as knowledge base.
  
      Returns:
        A tuple with:
          * the state that has the state of training (including current
            global_step, model_state, rng, and the optimizer)
          * a dictionary with the train_summary
          * a dictionary with the evaluation summary
      """
      host_id = jax.process_index()
      lead_host = host_id == 0
      # Build the loss_fn, metrics, and flax_model.
  
      model = model_cls(
          config, dataset.meta_data, kb_datasets=kb_datasets
      )
      train_state, start_step, chrono, lr_fn = init_state(
          model, dataset, config, workdir, rng, writer
      )
  
      logging.info(
          "Complete initialization for %s. Start Training.", host_id
      )
      train_state = jax_utils.replicate(train_state)
      local_kb.set_encode_fn(flax_model=model.flax_model)
      train_state = local_kb.update_memory(
          train_state,
          bsz=config.batch_size,
          data_k=model.data_k,
          retr_k=model.retr_k,
          axis_index_groups=model.axis_index_groups,
      )
      logging.info("Number of processes is %s", jax.process_count())
  
      # Get the pmapped train and eval steps.
      train_step_pmapped = jax.pmap(
          functools.partial(
              train_step,
              flax_model=model.flax_model,
              loss_fn=model.loss_function_dict,
              metrics_fn=model.get_metrics_fn("train"),
              debug=config.debug_train,
              model_config=config.model,
          ),
          axis_name="batch",
          # We can donate both buffers of train_state and train_batch.
          donate_argnums=(0, 1),
      )
      eval_step_pmapped = jax.pmap(
          functools.partial(
              eval_step,
              flax_model=model.flax_model,
              metrics_fn=model.get_metrics_fn("validation"),
              debug=config.debug_eval,
              model_config=config.model,
          ),
          axis_name="batch",
          # We can donate the eval_batch's buffer.
          donate_argnums=(1),
      )
      train_summary, eval_summary, e_metrics = {}, {}, {}
      eval_step_autoregressive_decoding_pmapped = jax.pmap(
          functools.partial(
              eval_step_autoregressive_decoding,
              model=model,
              metrics_fn=model.get_metrics_fn("validation"),
              model_config=config.model,
              vocab_size=config.vocab_size,
              num_decodes=config.autoregressive_decoding.num_decodes,
              beam_search=config.autoregressive_decoding.beam_search,
              debug=config.debug_eval,
          ),
          axis_name="batch",
          # We can donate the eval_batch's buffer.
          donate_argnums=(1),
      )
  
      # Calculate the total number of training steps.
      total_steps, steps_per_epoch = train_utils.get_num_training_steps(
          config, dataset.meta_data
      )
      log_eval_steps = config.get("log_eval_steps", steps_per_epoch)
      checkpoint_steps = config.get("checkpoint_steps", steps_per_epoch)
      log_summary_steps = config.get(
          "log_summary_steps", log_eval_steps
      )
      frozen_memory = config.get("frozen_memory", False)
  
      logging.info("Start training from step %d", start_step + 1)
      report_progress = periodic_actions.ReportProgress(
          num_train_steps=total_steps, writer=writer
      )
      hooks = [report_progress]
  
      # if config.get('xprof', True) and lead_host:
      #  hooks.append(periodic_actions.Profile(num_profile_steps=5, logdir=workdir))
  
      chrono.inform(
          int(start_step),
          int(total_steps),
          int(config.batch_size),
          int(steps_per_epoch),
      )
  
      summary_builder = trainer_utils.SummaryBuilder([], [])
  
      def write_note(note):
          if lead_host:
              platform.work_unit().set_notes(note)
  
      for step in range(start_step + 1, total_steps + 1):
          if lead_host:
              logging.info("training for step %d", step)
          ###################### Training ########################
          with jax.profiler.StepTraceAnnotation("train", step_num=step):
              train_batch = next(dataset.train_iter)
              inp_image_sample = np.asarray(
                  train_batch["encoder_input_image"][0][0]
              )
              train_batch["device_id"] = jnp.arange(
                  train_batch["encoder_input_image"].shape[0]
              )
              (
                  train_state,
                  t_metrics,
                  logs,
                  retr_top_image,
              ) = train_step_pmapped(train_state, train_batch)
              retr_top_image = np.asarray(retr_top_image[0][0])
          for hook in hooks:
              hook(step)
          logs["learning_rate"] = lr_fn(step).reshape([-1, 1])
          summary_builder.update(
              metrics_update=t_metrics, extra_logs_update=logs
          )
          jax.tree_map(lambda h: h.delete(), train_batch)
          if lead_host:
              logging.info("finish training for step %d", step)
          ###################### LOG TRAIN SUMMARY ########################
          if (step % log_summary_steps == 2) or (step == total_steps):
              chrono.pause()
              if lead_host:
                  logging.info("log training summary for step %d", step)
                  chrono.tick(step, writer, write_note)
                  writer.write_images(
                      step,
                      {
                          "train/retr_image": np.expand_dims(
                              retr_top_image, axis=0
                          ),
                          "train/query_image": np.expand_dims(
                              inp_image_sample, axis=0
                          ),
                      },
                  )
              writer.flush()
              train_summary = summary_builder.write(writer, step)
              chrono.resume()
          del inp_image_sample, retr_top_image
          ################### EVALUATION ################################
          should_eval = (step % log_eval_steps == 2) or (
              step == total_steps
          )
          if should_eval:
              # update the KB memory
              if not frozen_memory:
                  train_state = local_kb.update_memory(
                      train_state,
                      bsz=config.batch_size,
                      data_k=model.data_k,
                      retr_k=model.retr_k,
                      axis_index_groups=model.axis_index_groups,
                  )
              logging.info("Start validation!")
              chrono.pause(wait_for=(train_state.params))
              # Sync model state across replicas.
              for ds_name in dataset.valid_iter:
                  logging.info("Validate on %s", ds_name)
                  # Compute the number of evaluation steps per dataset.
                  num_eval_examples = dataset.meta_data[
                      "num_eval_examples"
                  ][ds_name]
                  total_eval_steps = int(
                      np.ceil(
                          num_eval_examples
                          / (config.get("eval_batch_size"))
                      )
                  )
                  steps_per_eval = config.get(
                      "steps_per_eval", total_eval_steps
                  )
                  for test_mode in range(2):
                      eval_metrics_all = []
                      eval_vqa_metrics_all = {}
                      for step_id in range(steps_per_eval):
                          eval_batch = next(dataset.valid_iter[ds_name])
                          inp_image_sample = np.asarray(
                              eval_batch["encoder_input_image"][0][0]
                          )
                          if test_mode == 0:
                              eval_batch["device_id"] = jnp.arange(
                                  eval_batch[
                                      "encoder_input_image"
                                  ].shape[0]
                              )
                          (
                              e_metrics,
                              retr_top_image,
                              predicted_logits,
                          ) = eval_step_pmapped(
                              train_state=train_state, batch=eval_batch
                          )
                          eval_metrics_all.append(
                              train_utils.unreplicate_and_get(e_metrics)
                          )
                          # add vqa accuracy metric if it's a vqa dataset
                          if config.model.get("qa", False):
                              eval_vqa_metric_fn = (
                                  model.get_vqa_metrics(
                                      predicted_logits, eval_batch
                                  )
                              )
                              eval_vqa_metrics = (
                                  eval_vqa_metric_fn.compute()
                              )
                              for key in eval_vqa_metrics:
                                  eval_vqa_metrics_all.setdefault(
                                      key, 0.0
                                  )
                                  eval_vqa_metrics_all[
                                      key
                                  ] += eval_vqa_metrics[key]
  
                          if (
                              test_mode == 0
                              and step_id == 0
                              and lead_host
                          ):
                              logging.info(e_metrics)
                              if ds_name == "val_cc":
                                  retr_top_image = np.asarray(
                                      retr_top_image[0][0]
                                  )
                                  writer.write_images(
                                      step,
                                      {
                                          "eval/retr_image": np.expand_dims(
                                              retr_top_image, axis=0
                                          ),
                                          "eval/query_image": np.expand_dims(
                                              inp_image_sample, axis=0
                                          ),
                                      },
                                  )
                                  writer.flush()
                                  del retr_top_image
                          jax.tree_map(lambda h: h.delete(), eval_batch)
                          del inp_image_sample
                      for key in eval_vqa_metrics_all:
                          eval_vqa_metrics_all[key] /= float(
                              steps_per_eval
                          )
                      if test_mode == 0:
                          eval_summary = train_utils.log_eval_summary(
                              step=step,
                              eval_metrics=eval_metrics_all,
                              extra_eval_summary=eval_vqa_metrics_all,
                              writer=writer,
                              prefix=ds_name,
                          )
                      else:
                          eval_summary = train_utils.log_eval_summary(
                              step=step,
                              eval_metrics=eval_metrics_all,
                              extra_eval_summary=eval_vqa_metrics_all,
                              writer=writer,
                              prefix="random_retrieve_" + ds_name,
                          )
                  # autoregressive eval
                  eval_metrics_all = []
                  eval_vqa_metrics_all = {}
                  for step_id in range(steps_per_eval):
                      eval_batch = next(dataset.valid_iter[ds_name])
                      eval_batch["device_id"] = jnp.arange(
                          eval_batch["encoder_input_image"].shape[0]
                      )
                      logging.log_first_n(
                          logging.INFO,
                          "Peforming eval with autoregressive decode",
                          3,
                      )
                      (
                          e_metrics,
                          retr_top_image,
                          predicted_logits,
                      ) = eval_step_autoregressive_decoding_pmapped(
                          train_state=train_state, batch=eval_batch
                      )
                      eval_metrics_all.append(
                          train_utils.unreplicate_and_get(e_metrics)
                      )
                      # add vqa accuracy metric if it's a vqa dataset
                      if config.model.get("qa", False):
                          eval_vqa_metric_fn = model.get_vqa_metrics(
                              predicted_logits, eval_batch
                          )
                          eval_vqa_metrics = (
                              eval_vqa_metric_fn.compute()
                          )
                          for key in eval_vqa_metrics:
                              eval_vqa_metrics_all.setdefault(key, 0.0)
                              eval_vqa_metrics_all[
                                  key
                              ] += eval_vqa_metrics[key]
                  for key in eval_vqa_metrics_all:
                      eval_vqa_metrics_all[key] /= float(steps_per_eval)
                  eval_summary = train_utils.log_eval_summary(
                      step=step,
                      eval_metrics=eval_metrics_all,
                      extra_eval_summary=eval_vqa_metrics_all,
                      writer=writer,
                      prefix=ds_name + "_autoregressive",
                  )
  
              chrono.resume()
              gc.collect()
          ##################### CHECKPOINTING ###########################
          if not config.checkpoint:
              continue
          elif (
              step % checkpoint_steps == 0
              and step > 0
              or (step == total_steps)
          ):
              logging.info("Save checkpoint!")
              chrono.pause(
                  wait_for=(train_state.params, train_state.opt_state)
              )
              with report_progress.timed("checkpoint"):
                  if lead_host:
                      logging.info(
                          "checkpointing (training step: %d)", step
                      )
                      # Take the first replica.
                      unrep_train_state = jax_utils.unreplicate(
                          train_state
                      )
                      (
                          unrep_train_state,
                          mem,
                      ) = trainer_utils.pop_axes_names(
                          unrep_train_state, axes_name="memory"
                      )
                      metadata = unrep_train_state.metadata
                      metadata["chrono"] = chrono.save()
                      unrep_train_state.replace(
                          metadata=metadata
                      )  # pytype: disable=attribute-error
                      train_utils.save_checkpoint(
                          workdir, unrep_train_state
                      )
                      del unrep_train_state, mem
              chrono.resume()
              logging.info("Checkpoint saved!")
  
      # Wait until computations are done before exiting.
      train_utils.barrier_across_hosts()
  
      # Return the train and eval summary after last step.
      return train_state, train_summary, eval_summary
  
imports: import functools
  import gc
  from typing import Any, Dict, Optional, Tuple
  from absl import logging
  from clu import metric_writers
  from clu import periodic_actions
  from clu import platform
  from flax import jax_utils
  import flax.linen
  from flax.training import common_utils
  import jax
  import jax.example_libraries.optimizers
  import jax.numpy
  import jax.profiler
  import ml_collections
  import numpy
  import optax
  from scenic.dataset_lib import dataset_utils
  from scenic.xm import xm_utils
  from scenic.model_lib.base_models import base_model
  from scenic.projects.knowledge_visual_language import trainer_utils
  from scenic.projects.knowledge_visual_language.models import constants
  from scenic.projects.knowledge_visual_language.models import local_memory
  from scenic.train_lib import lr_schedules
  from scenic.train_lib import optimizers
  from scenic.train_lib import pretrain_utils
  from scenic.train_lib import train_utils
label_desc: Use image node_id 5 for calling a global function with example usage: train_and_eval() and returns: train_state,  train_summary,  eval_summary