code: def get_train_step(
      apply_fn: Callable[..., Tuple[PyTree, PyTree]],
      loss_and_metrics_fn: Callable[..., Tuple[PyTree, PyTree]],
      tx: optax.GradientTransformation,
      update_batch_stats: bool = False,
      debug: bool = False,
  ):
      """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) argument is
      donated to the computation.
  
      Args:
        apply_fn: Model application function.
        loss_and_metrics_fn: Function to calculate loss and metrics.
        tx: An optax.GradientTransformation.
        update_batch_stats: Whether to update batch statistics in BatchNorm
          during training or freeze it.
        debug: Whether the debug mode is enabled during training. `debug=True`
          enables model specific logging/storing some values using
          jax.host_callback.
  
      Returns:
        Train step function that takes a train_state and batch and returns
        new_train_state, metrics, predictions.
  
      """
  
      def train_step(train_state, batch):
          def loss_fn(params):
              new_rng, rng = jax.random.split(train_state.rng)
              # Bind the rng to the host/device we are on.
              model_rng = train_utils.bind_rng_to_host_device(
                  rng, axis_name="batch", bind_to="device"
              )
              variables = {"params": params, **train_state.model_state}
              predictions, mutated_variables = apply_fn(
                  variables,
                  batch["inputs"],
                  padding_mask=batch["padding_mask"],
                  update_batch_stats=update_batch_stats,
                  mutable=train_state.model_state.keys(),
                  train=True,
                  rngs={"dropout": model_rng},
                  debug=debug,
              )
              loss, metrics = loss_and_metrics_fn(
                  predictions, batch, model_params=variables["params"]
              )
              return loss, (
                  mutated_variables,
                  metrics,
                  predictions,
                  new_rng,
              )
  
          new_global_step = train_state.global_step + 1
          (
              _,
              (new_model_state, metrics, predictions, new_rng),
          ), grads = jax.value_and_grad(loss_fn, has_aux=True)(
              train_state.params
          )
  
          # Re-use same axis_name as in the call to `pmap(...train_step...)` below.
          grads = jax.lax.pmean(grads, axis_name="batch")
  
          updates, new_opt_state = tx.update(
              grads, train_state.opt_state, params=train_state.params
          )
          new_params = optax.apply_updates(train_state.params, updates)
          train_state = train_state.replace(
              global_step=new_global_step,
              params=new_params,
              opt_state=new_opt_state,
              model_state=new_model_state,
              rng=new_rng,
          )
          return train_state, metrics, predictions
  
      return train_step
  
imports: from collections.abc import Mapping
  from concurrent import futures
  import functools
  import time
  from typing import Any, Callable, Dict, Sequence, Tuple, Type, Union
  from absl import logging
  from clu import metric_writers
  from clu import periodic_actions
  import flax
  from flax import jax_utils
  from flax.training.checkpoints import restore_checkpoint
  import jax
  import jax.numpy
  import jax.profiler
  import ml_collections
  import numpy
  import optax
  from scenic.dataset_lib import dataset_utils
  from scenic.projects.baselines.deformable_detr import coco_eval
  from scenic.projects.baselines.deformable_detr import evaluate
  from scenic.projects.baselines.deformable_detr.model import DeformableDETRModel
  from scenic.projects.baselines.detr import train_utils
  from scenic.train_lib import pretrain_utils
  from scenic.train_lib import train_utils
  from scenic.train_lib.train_utils import PyTree
  from scenic.train_lib.train_utils import TrainState
label_desc: Use image node_id 2 for calling a global function with example usage: get_train_step(apply_fn, loss_and_metrics_fn, tx, update_batch_stats, debug) and returns: train_step, train_state,  metrics,  predictions, loss,  mutated_variables,  metrics,  predictions,  new_rng