code: def training_step(optimizer, batch, dropout_rng):
      dropout_rng, new_dropout_rng = jax.random.split(dropout_rng)
  
      def loss_fn(params):
          targets = batch.pop("labels")
  
          # Hide away tokens which doesn't participate in the optimization
          token_mask = jnp.where(targets > 0, 1.0, 0.0)
  
          logits = model(
              **batch,
              params=params,
              dropout_rng=dropout_rng,
              train=True,
          )[0]
          loss, weight_sum = cross_entropy(logits, targets, token_mask)
          return loss / weight_sum
  
      step = optimizer.state.step
      lr = lr_scheduler_fn(step)
      grad_fn = jax.value_and_grad(loss_fn)
      loss, grad = grad_fn(optimizer.target)
      grad = jax.lax.pmean(grad, "batch")
      optimizer = optimizer.apply_gradient(grad, learning_rate=lr)
  
      return loss, optimizer, new_dropout_rng
  
imports: import logging
  import os
  import sys
  from dataclasses import dataclass, field
  from pathlib import Path
  from typing import Dict, List, Optional, Tuple
  import numpy
  from datasets import load_dataset
  from tqdm import tqdm
  import jax
  import jax.numpy
  from flax import jax_utils
  from flax.optim import Adam
  from flax.training import common_utils
  from flax.training.common_utils import get_metrics
  from jax.nn import log_softmax
  from modeling_flax_performer import FlaxPerformerForMaskedLM
  from transformers import MODEL_FOR_MASKED_LM_MAPPING, AutoTokenizer, BertConfig, FlaxBertForMaskedLM, HfArgumentParser, PreTrainedTokenizerBase, TensorType, TrainingArguments, is_tensorboard_available, set_seed
label_desc: Use image node_id 5 for calling a global function with example usage: training_step(optimizer, batch, dropout_rng) and returns: loss,  optimizer,  new_dropout_rng, unknown