code: def train_step(
      train_state: train_utils.TrainState,
      batch: Batch,
      *,
      flax_model: nn.Module,
      loss_fn: LossFn,
      lr_fn: LrFn,
      metrics_fn: MetricFn,
      config: ml_collections.ConfigDict,
      debug: Optional[bool] = False,
  ) -> Tuple[
      train_utils.TrainState,
      Dict[str, Tuple[float, int]],
      Dict[str, jnp.ndarray],
      Dict[str, jnp.ndarray],
  ]:
      """Runs a single step of training.
  
      Given the state of the training and a batch of data, computes
      the loss and updates the parameters of the model.
  
      Note that in this code, the buffers of the first (train_state) and second
      (batch) arguments are donated to the computation.
  
      Args:
        train_state: The state of training including the current
          global_step, model_state, rng, and optimizer. The buffer of this argument
          can be donated to the computation.
        batch: A single batch of data. The buffer of this argument can be donated to
          the computation.
        flax_model: A Flax model.
        loss_fn: A loss function that given logits, a batch, and parameters of the
          model calculates the loss.
        lr_fn: The learning rate fn used for the logging the learning rate.
        metrics_fn: A metrics function that given logits and batch of data,
          calculates the metrics as well as the loss.
        config: Configurations of the experiment.
        debug: Whether the debug mode is enabled during training. `debug=True`
          enables model specific logging/storing some values using
          jax.host_callback.
  
      Returns:
        Updated state of training, computed metrics, and learning rate for logging.
      """
      new_rng, rng = jax.random.split(train_state.rng)
  
      images_to_log = {}
      images_to_log["input_image_before_mixup"] = unnormalize_imgnet(
          batch["inputs"]
      )
  
      if config.get("mixup") and config.mixup.alpha:
          mixup_rng, rng = jax.random.split(rng, 2)
          mixup_rng = train_utils.bind_rng_to_host_device(
              mixup_rng,
              axis_name="batch",
              bind_to=config.mixup.get("bind_to", "device"),
          )
          batch = dataset_utils.mixup(
              batch,
              config.mixup.alpha,
              config.mixup.get("image_format", "NHWC"),
              rng=mixup_rng,
          )
  
      images_to_log["input_image_after_mixup"] = unnormalize_imgnet(
          batch["inputs"]
      )
  
      # Bind the rng to the host/device we are on.
      dropout_rng = train_utils.bind_rng_to_host_device(
          rng, axis_name="batch", bind_to="device"
      )
  
      def training_loss_fn_single(
          params,
          model_state,
          batch,
          use_aux_batchnorm,
          use_aux_dropout,
          train_var=True,
      ):
          variables = {"params": params, **model_state}
          apply_kwargs = models.get_kwargs(
              config, use_aux_batchnorm, use_aux_dropout
          )
          logits, new_model_state = flax_model.apply(
              variables,
              batch["inputs"],
              mutable=["batch_stats"],
              train=train_var,
              rngs={"dropout": dropout_rng},
              debug=debug,
              **apply_kwargs,
          )
          loss = loss_fn(logits, batch, variables["params"])
          return loss, (new_model_state, logits)
  
      if not config.adversarial_augmentation_mode:
  
          def training_loss_fn(params, model_state):
              total_loss, (
                  new_model_state,
                  logits,
              ) = training_loss_fn_single(
                  params,
                  model_state,
                  batch,
                  use_aux_batchnorm=False,
                  use_aux_dropout=False,
              )
              misc_artifacts = {}
              return total_loss, (
                  new_model_state,
                  logits,
                  misc_artifacts,
              )
  
      elif config.adversarial_augmentation_mode == "advprop_pyramid":
  
          def training_loss_fn(params, model_state):
              clean_inputs = batch["inputs"]
              clean_loss, (
                  clean_model_state,
                  clean_logits,
              ) = training_loss_fn_single(
                  params,
                  model_state,
                  batch,
                  use_aux_batchnorm=False,
                  use_aux_dropout=False,
              )
  
              (
                  adv_image,
                  adv_perturbation,
                  misc_artifacts,
              ) = attack_compute.get_adversarial_image_and_perturbation(
                  batch=batch,
                  clean_logits=clean_logits,
                  config=config,
                  training_loss_fn_single=training_loss_fn_single,
                  train_state=train_state,
                  dropout_rng=dropout_rng,
                  lr_rng=rng,
              )
  
              adv_batch = batch
              adv_batch["inputs"] = adv_image
              adv_loss, (
                  adv_model_state,
                  adv_logits,
              ) = training_loss_fn_single(
                  params,
                  clean_model_state,
                  adv_batch,
                  use_aux_batchnorm=True,
                  use_aux_dropout=True,
              )
  
              adv_loss_weight = config.advprop.adv_loss_weight
              total_loss = clean_loss + adv_loss_weight * adv_loss
  
              # set up misc_artifacts
              misc_artifacts["logits"] = clean_logits
              misc_artifacts["adv_logits"] = adv_logits
              misc_artifacts["image_diffs"] = (
                  adv_image - clean_inputs
              ).sum(axis=(1, 2, 3))
              misc_artifacts["adv_loss_weight"] = adv_loss_weight
              misc_artifacts["adv_logits"] = adv_logits
              misc_artifacts["loss"] = clean_loss
              misc_artifacts["adv_loss"] = adv_loss
              misc_artifacts["adv_image"] = adv_image
              misc_artifacts["adv_perturbation"] = adv_perturbation
  
              return total_loss, (
                  adv_model_state,
                  clean_logits,
                  misc_artifacts,
              )
  
      else:
          raise NotImplementedError(
              "Unrecognized adversarial augmentation mode."
          )
  
      compute_gradient_fn = jax.value_and_grad(
          training_loss_fn, has_aux=True
      )
  
      computed_gradient = compute_gradient_fn(
          train_state.params, train_state.model_state
      )
  
      (
          train_cost,
          (new_model_state, logits, misc_artifacts),
      ), grad = computed_gradient
  
      metrics = metrics_fn(logits, batch)
      if not config.adversarial_augmentation_mode:
          metrics, images_to_log = attack_metrics.get_metrics(
              misc_artifacts, batch, metrics, images_to_log, metrics_fn
          )
  
      del train_cost
  
      # Re-use same axis_name as in the call to `pmap(...train_step...)` below.
      grad = jax.lax.pmean(grad, axis_name="batch")
  
      if config.get("max_grad_norm", None) is not None:
          grad = clip_grads(grad, config.max_grad_norm)
  
      updates, new_opt_state = train_state.tx.update(
          grad, train_state.opt_state, train_state.params
      )
      new_params = optax.apply_updates(
          params=train_state.params, updates=updates
      )
  
      training_logs = {}
      training_logs["learning_rate"] = lr_fn(train_state.global_step)
  
      new_train_state = (
          train_state.replace(  # pytype: disable=attribute-error
              global_step=train_state.global_step + 1,
              opt_state=new_opt_state,
              params=new_params,
              model_state=new_model_state,
              rng=new_rng,
          )
      )
      return new_train_state, metrics, training_logs, images_to_log
  
imports: import functools
  from typing import Any, Callable, Dict, Optional, Tuple, Type
  from clu import metric_writers
  from clu import periodic_actions
  from clu import platform
  from flax import jax_utils
  import flax.linen
  import jax
  from jax.example_libraries.optimizers import clip_grads
  import jax.numpy
  import jax.profiler
  import ml_collections
  import numpy
  import optax
  from scenic.dataset_lib import dataset_utils
  from scenic.model_lib.base_models import base_model
  from scenic.projects.adversarialtraining import train_utils
  from scenic.projects.adversarialtraining.attacks import attack_compute
  from scenic.projects.adversarialtraining.attacks import attack_metrics
  from scenic.projects.adversarialtraining.models import models
  from scenic.train_lib import lr_schedules
  from scenic.train_lib import optimizers
  from scenic.train_lib import train_utils
label_desc: Use image node_id 2 for calling a global function with example usage: train_step(train_state, batch) and returns: new_train_state,  metrics,  training_logs,  images_to_log, loss,  new_model_state,  logits, total_loss,  new_model_state,  logits,  misc_artifacts, total_loss,  adv_model_state,  clean_logits,  misc_artifacts