code: def get_train_step(
      flax_model, loss_and_metrics_fn, config, debug=False
  ):
      """Runs a single step of training.
  
      Given the state of the training and a batch of data, the train step computes
      the loss and updates the parameters of the model.
  
      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.
        config: Experiment config dictionary.
        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 and metrics.
      """
      # Get shorthands from the config.
      optax_grad_pmean = config.optimizer.get("optax_grad_pmean", False)
      per_example_clipping = config.optimizer.get(
          "per_example_clipping", False
      )
      max_grad_norm = config.optimizer.get("max_grad_norm")
  
      def update_fn(train_state, grad, new_rng):
          step = train_state.global_step
  
          # In case of per-example gradients, we need to aggregate them after
          # clipping. This is implemented as an Optax tx.
          if not per_example_clipping:
              assert not optax_grad_pmean, (
                  "Optax gradient aggregation should only be"
                  " used with per-example gradients."
              )
  
              # Re-use same axis_name as in the call to `pmap(...train_step...)` below.
              grad = jax.lax.pmean(grad, axis_name="batch")
  
          updates, new_opt_state = train_state.tx.update(
              grad, train_state.opt_state, train_state.params
          )
          new_params = optax.apply_updates(train_state.params, updates)
  
          new_train_state = train_state.replace(
              global_step=step + 1,
              opt_state=new_opt_state,
              params=new_params,
              rng=new_rng,
          )
          return new_train_state, grad
  
      def train_step(train_state, batch):
          def grad_fn(inputs):
              batch, rng = inputs["batch"], inputs["rng"]
  
              def loss_fn(params):
                  # 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"
                  )
                  kwargs = {"text_queries": batch["queries"]}
  
                  predictions = flax_model.apply(
                      {"params": params, **train_state.model_state},
                      batch["inputs"],
                      train=True,
                      debug=debug,
                      rngs={"dropout": model_rng},
                      **kwargs,
                  )
  
                  return loss_and_metrics_fn(
                      predictions, batch, model_params=params
                  )
  
              compute_gradient_fn = jax.value_and_grad(
                  loss_fn, has_aux=True
              )
              (_, metrics), grad = compute_gradient_fn(
                  train_state.params
              )
  
              # Note: zero-ing out frozen gradients changes the L2 norm. Clipping is
              # done inside Optax before zero-inng out frozen weights.
              grad = scenic_optax.replace_frozen(
                  config.schedule, grad, 0.0
              )
              metrics["l2_grads_orig"] = (utils.l2_norm(grad), 1)
              return grad, metrics
  
          if per_example_clipping and max_grad_norm is not None:
              # For per-example clipping we produce per-example rngs.
              rngs = jax.random.split(
                  train_state.rng, num=batch["inputs"].shape[0] + 1
              )
              new_rng, model_rng = rngs[0], rngs[1:]
              # We add an additional dimension which wil serve as the batch dimension
              # for single examples when applying scan or vmap.
              batch = jax.tree_util.tree_map(
                  lambda x: x[:, jnp.newaxis], batch
              )
              inp = {"batch": batch, "rng": model_rng}
              grad, metrics = jax.vmap(grad_fn, 0)(inp)
          else:
              # Without per example clipping we can just compute the gradient on the
              # entire batch.
              new_rng, model_rng = jax.random.split(train_state.rng)
              grad, metrics = grad_fn(
                  {"batch": batch, "rng": model_rng}
              )
  
          new_train_state, g = update_fn(train_state, grad, new_rng)
          metrics["l2_grads"] = (utils.l2_norm(g), 1)
          metrics["l2_params"] = (
              utils.l2_norm(new_train_state.params),
              1,
          )
          return new_train_state, metrics
  
      return train_step
  
imports: from concurrent import futures
  import functools
  import time
  from typing import Any
  from absl import logging
  from clu import metric_writers
  from clu import periodic_actions
  from clu import platform
  import flax
  from flax import jax_utils
  from flax.training import checkpoints
  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.owl_vit import utils
  from scenic.train_lib import optax
  from scenic.train_lib import train_utils
label_desc: Use image node_id 1 for calling a global function with example usage: get_train_step(flax_model, loss_and_metrics_fn, config, debug) and returns: train_step, new_train_state,  grad, new_train_state,  metrics, grad,  metrics, loss_and_metrics_fn