code: def train_step(
      state: TrainState,
      batch: Dict[str, Array],
      rngs: Dict[str, Any],
  ) -> Tuple[TrainState, Metrics]:
      """Train for a single step."""
      # Make sure to get a new RNG at every step.
      step = state.step
      rngs = {
          name: jax.random.fold_in(rng, step)
          for name, rng in rngs.items()
      }
  
      def loss_fn(params):
          variables = {"params": params}
          logits = state.apply_fn(
              variables,
              batch["token_ids"],
              batch["length"],
              deterministic=False,
              rngs=rngs,
          )
  
          labels = batch["label"]
          if labels.ndim == 1:
              labels = jnp.expand_dims(labels, 1)
          loss = jnp.mean(
              sigmoid_cross_entropy_with_logits(
                  labels=labels, logits=logits
              )
          )
          return loss, logits
  
      grad_fn = jax.value_and_grad(loss_fn, has_aux=True)
      value, grads = grad_fn(state.params)
      (_, logits) = value
  
      new_state = state.apply_gradients(grads=grads)
      metrics = compute_metrics(labels=batch["label"], logits=logits)
      return new_state, metrics
  
imports: from typing import Any, Callable, Dict, Iterable, Optional, Sequence, Tuple, Union
  from absl import logging
  from flax import struct
  from flax.metrics import tensorboard
  from flax.training import train_state
  import jax
  import jax.numpy
  import ml_collections
  import numpy
  import optax
  import tensorflow
  import input_pipeline
  import models
label_desc: Use image node_id 6 for calling a global function with example usage: train_step(state, batch, rngs) and returns: new_state,  metrics, loss,  logits