code: def train_step(
      state, batch, model, learning_rate_fn, dropout_rng=None
  ):
      """Perform a single training step."""
      train_keys = ["inputs", "targets"]
      (inputs, targets) = (batch.get(k, None) for k in train_keys)
  
      weights = jnp.where(targets > 0, 1, 0).astype(jnp.float32)
  
      dropout_rng = jax.random.fold_in(dropout_rng, state.step)
  
      def loss_fn(params):
          """loss function used for training."""
          logits = model.apply(
              {"params": params},
              inputs=inputs,
              train=True,
              rngs={"dropout": dropout_rng},
          )
          loss, weight_sum = compute_weighted_cross_entropy(
              logits, targets, weights
          )
  
          mean_loss = loss / weight_sum
          return mean_loss, logits
  
      lr = learning_rate_fn(state.step)
      grad_fn = jax.value_and_grad(loss_fn, has_aux=True)
      (_, logits), grads = grad_fn(state.params)
      grads = jax.lax.pmean(grads, "batch")
      new_state = state.apply_gradients(grads=grads)
      metrics = compute_metrics(logits, targets, weights)
      metrics["learning_rate"] = lr
  
      return new_state, metrics
  
imports: import functools
  import os
  import time
  from absl import app
  from absl import flags
  from absl import logging
  from flax import jax_utils
  from flax import linen
  from flax.metrics import tensorboard
  from flax.training import common_utils
  from flax.training import train_state
  import jax
  import jax.numpy
  from jax import random
  import numpy
  import optax
  import tensorflow
  import input_pipeline
  import models
label_desc: Use image node_id 5 for calling a global function with example usage: train_step(state, batch, model, learning_rate_fn, dropout_rng) and returns: new_state,  metrics, mean_loss,  logits