code: def eval_step(params, batch):
      """
      Calculate evaluation metrics on a batch.
      """
      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, train=False)[0]
  
      return compute_metrics(logits, targets, token_mask)
  
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 6 for calling a global function with example usage: eval_step(params, batch) and returns: compute_metrics