code: def write_eval_metric(summary_writer, eval_metrics, step):
      for metric_name, value in eval_metrics.items():
          summary_writer.scalar(f"eval_{metric_name}", value, step)
  
imports: import json
  import logging
  import math
  import os
  import sys
  import time
  from dataclasses import asdict, dataclass, field
  from enum import Enum
  from itertools import chain
  from pathlib import Path
  from typing import Dict, List, Optional
  import numpy
  from datasets import load_dataset
  from tqdm import tqdm
  import flax
  import jax
  import jax.numpy
  import optax
  from flax import jax_utils, traverse_util
  from flax.jax_utils import pad_shard_unpad
  from flax.training import train_state
  from flax.training.common_utils import get_metrics, onehot, shard
  from huggingface_hub import Repository
  from transformers import CONFIG_MAPPING, FLAX_MODEL_FOR_MASKED_LM_MAPPING, AutoTokenizer, BatchEncoding, FlaxT5ForConditionalGeneration, HfArgumentParser, PreTrainedTokenizerBase, T5Config, is_tensorboard_available, set_seed
  from transformers.models.t5.modeling_flax_t5 import shift_tokens_right
  from transformers.utils import get_full_repo_name, send_example_telemetry
label_desc: Use image node_id 4 for calling a global function with example usage: write_eval_metric(summary_writer, eval_metrics, step) without return types