code: def get_train_step(
      flax_model,
      loss_and_metrics_fn,
      learning_rate_fn,
      backbone_learning_rate_fn,
      max_grad_norm=None,
      update_model_state=False,
      debug=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) and second
      (batch) arguments are donated to the computation.
  
      Args:
        flax_model: Flax model (an instance of nn.Module).
        loss_and_metrics_fn: A function that given model predictions, a batch, and
          parameters of the model calculates the loss as well as metrics.
        learning_rate_fn: Learning rate scheduler which given the global_step
          generates the learning rate.
        backbone_learning_rate_fn: Learning rate scheduler which given the
          global_step generates the learning rate used for updating  the parameters
          of the backbone in the model.
        max_grad_norm: float; Maximum gradient norm used for gradient clipping. If
          set to None, no gradient clipping happens.
        update_model_state: bool; whether to update the model_state (e.g. batch
          stats in BatchNorm) during training or freeze it.
        debug: bool; 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, lr, predictions.
  
      """
      backbone_learning_rate_fn = (
          backbone_learning_rate_fn or learning_rate_fn
      )
  
      def update_fn(train_state, new_model_state, grad, new_rng):
          step = train_state.global_step
          backbone_lr = backbone_learning_rate_fn(step)
          lr = learning_rate_fn(step)
  
          # Re-use same axis_name as in the call to `pmap(...train_step...)` below.
          grad = jax.lax.pmean(grad, axis_name="batch")
  
          if max_grad_norm is not None:
              grad = detr_train_utils.clip_grads(grad, max_grad_norm)
  
          (
              backbone_opt_hps,
              opt_hps,
          ) = train_state.optimizer.optimizer_def.hyper_params
          new_optimizer = train_state.optimizer.apply_gradient(
              grad,
              hyper_params=[
                  backbone_opt_hps.replace(learning_rate=backbone_lr),
                  opt_hps.replace(learning_rate=lr),
              ],
          )
          new_train_state = train_state.replace(
              global_step=step + 1,
              optimizer=new_optimizer,
              model_state=new_model_state,
              rng=new_rng,
          )
          return new_train_state, lr, backbone_lr
  
      def train_step(train_state, batch, task):
          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, new_model_state = flax_model.apply(
                  variables,
                  image=batch["inputs"],
                  obj_mask=batch["label"]["obj_mask"],
                  desc_id=batch["label"]["desc_id"],
                  resource_id=batch["label"]["resource_id"],
                  name_id=batch["label"]["name_id"],
                  boxes=batch["label"]["boxes"],
                  task=task,
                  padding_mask=batch["padding_mask"],
                  update_batch_stats=update_model_state,
                  mutable=["batch_stats"],
                  train=True,
                  rngs={"dropout": model_rng},
                  debug=debug,
              )
              if task == "layout_denoise":
                  _pred_probs(predictions)
  
              loss, metrics = loss_and_metrics_fn(
                  predictions,
                  batch,
                  task=task,
                  model_params=variables["params"],
              )
              return loss, (
                  new_model_state,
                  metrics,
                  predictions,
                  new_rng,
              )
  
          compute_gradient_fn = jax.value_and_grad(
              loss_fn, has_aux=True
          )
          (_, aux), grad = compute_gradient_fn(
              train_state.optimizer.target
          )
          new_model_state, metrics, predictions, new_rng = aux
          new_train_state, lr, backbone_lr = update_fn(
              train_state, new_model_state, grad, new_rng
          )
          return new_train_state, metrics, lr, backbone_lr, predictions
  
      return train_step
  
imports: import collections
  from concurrent import futures
  import functools
  import random
  import time
  from typing import Any, Dict, Tuple, Optional
  from absl import logging
  from clu import metric_writers
  from clu import periodic_actions
  from flax import jax_utils
  import flax.linen
  from flax.training.checkpoints import restore_checkpoint
  import jax
  import jax.numpy
  import jax.profiler
  import ml_collections
  import numpy
  from scenic.dataset_lib import dataset_utils
  from scenic.projects.baselines.detr import train_utils
  from scenic.projects.layout_denoise import train_utils
  from scenic.projects.layout_denoise.datasets import dataset
  from scenic.train_lib_deprecated import lr_schedules
  from scenic.train_lib_deprecated import pretrain_utils
  from scenic.train_lib_deprecated import train_utils
label_desc: Use image node_id 2 for calling a global function with example usage: get_train_step(flax_model, loss_and_metrics_fn, learning_rate_fn, backbone_learning_rate_fn, max_grad_norm, update_model_state, debug) and returns: train_step, new_train_state,  lr,  backbone_lr, new_train_state,  metrics,  lr,  backbone_lr,  predictions, loss,  new_model_state,  metrics,  predictions,  new_rng