code: def train_step(
      train_state: train_utils.TrainState,
      batch: Batch,
      *,
      flax_model: nn.Module,
      loss_fn: Any,
      metrics_fn: MetricFn,
      config: ml_collections.ConfigDict,
      debug: Optional[bool] = False,
      learn_token_score: bool = False,
      add_boxes: bool = False,
  ) -> Tuple[train_utils.TrainState, Dict[str, Any]]:
      """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.
        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.
        learn_token_score: if enable learning token score. The network will have
          additional outputs of each scores for each token.
        add_boxes: if add boxes in shape batch x time x num_objs x 4
  
      Returns:
        Updated state of training, computed metrics, and learning rate for logging.
      """
      new_rng, rng = jax.random.split(train_state.rng)
  
      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 = custom_train_utils.mixup_cutmix(
              batch,
              mixup_rng,
              config.mixup.alpha,
              cutmix_alpha=config.mixup.get("cutmix_alpha", 0.0),
              switch_prob=config.mixup.get("cutmix_switch_prob", 0.5),
              label_smoothing=config.mixup.get("label_smoothing", 0.0),
          )
  
      # Bind the dropout 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(params):
          variables = {"params": params, **train_state.model_state}
          outputs, new_model_state = flax_model.apply(
              variables,
              batch["inputs"],
              boxes=batch["bboxes"] if add_boxes else None,
              mutable=["batch_stats"],
              train=True,
              rngs={"dropout": dropout_rng},
              debug=debug,
          )
          if learn_token_score:
              logits = outputs["logits"]
              cls_loss, token_loss = loss_fn(
                  logits,
                  batch,
                  variables["params"],
                  aux_outputs=outputs,
              )
              loss = cls_loss + token_loss
              return loss, (
                  new_model_state,
                  outputs,
                  cls_loss,
                  token_loss,
              )
          else:
              loss = loss_fn(outputs, batch, variables["params"])
              return loss, (new_model_state, outputs, None, None)
  
      compute_gradient_fn = jax.value_and_grad(
          training_loss_fn, has_aux=True
      )
      (
          _,
          (new_model_state, outputs, cls_loss, token_loss),
      ), grad = compute_gradient_fn(train_state.params)
  
      if learn_token_score:
          logits = outputs["logits"]
          metrics = metrics_fn(logits, batch)
          metrics["cls_loss"] = (cls_loss, 1.0)
          metrics["token_loss"] = (token_loss, 1.0)
          token_scores = outputs["token_scores"]
          gt_scores = outputs["gt_scores"]
          n_tokens = token_scores.shape[-1]
          k = int(
              config.model.object_config.get("keep_token_ratio", 0.5)
              * n_tokens
          )
          topk_val, _ = jax.lax.top_k(token_scores, k)
          pred_mask = token_scores > topk_val[:, -1:]
          gt_topk_val, _ = jax.lax.top_k(gt_scores, k)
          gt_mask = gt_scores > gt_topk_val[:, -1:]
          acc = (pred_mask == gt_mask).sum() / pred_mask.size
          metrics["token_acc"] = (acc, 1.0)
      else:
          metrics = metrics_fn(outputs, batch)
  
      if add_boxes:
          metrics["boxes_num"] = (
              (batch["bboxes"] > 0).any(axis=3).sum()
              / (batch["bboxes"].shape[0] * batch["bboxes"].shape[1]),
              1.0,
          )
  
      # Re-use same axis_name as in the call to `pmap(...train_step...)` below.
      grad = jax.lax.pmean(grad, axis_name="batch")
  
      metrics["max_grad_norm_preclip"] = (
          custom_train_utils.compute_max_norm(grad),
          1,
      )
      if config.get("max_grad_norm", None) is not None:
          grad = clip_grads(grad, config.max_grad_norm)
          metrics["max_grad_norm_postclip"] = (
              custom_train_utils.compute_max_norm(grad),
              1,
          )
  
      # We no longer perform explicit weight decay here. This can be added
      # as an Optax gradient transformation if necessary. Or one can also use
      # AdamW instead of Adam.
      updates, new_opt_state = train_state.tx.update(
          grad, train_state.opt_state, train_state.params
      )  # pytype: disable=attribute-error
      new_params = optax.apply_updates(train_state.params, updates)
  
      # Log additional statistics. These are the L2 norms of the entire flattened
      # vector.
      metrics["l2_grads"] = (
          jnp.sqrt(
              sum([jnp.vdot(g, g) for g in jax.tree_leaves(grad)])
          ),
          1,
      )
      metrics["l2_params"] = (
          jnp.sqrt(
              sum([jnp.vdot(p, p) for p in jax.tree_leaves(new_params)])
          ),
          1,
      )
      metrics["l2_updates"] = (
          jnp.sqrt(
              sum([jnp.vdot(u, u) for u in jax.tree_leaves(updates)])
          ),
          1,
      )
  
      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
  
imports: import functools
  from typing import Any, Callable, Dict, Iterator, Optional, Tuple, Type, Union
  from absl import logging
  from clu import metric_writers
  from clu import periodic_actions
  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.common_lib import debug_utils
  from scenic.dataset_lib import dataset_utils
  from scenic.model_lib.base_models import base_model
  from scenic.projects.objectvivit import optimizer_utils
  from scenic.projects.objectvivit import train_utils
  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 1 for calling a global function with example usage: train_step(train_state, batch) and returns: new_train_state,  metrics, loss,  new_model_state,  outputs,  cls_loss,  token_loss, loss,  new_model_state,  outputs,  None,  None