code: def construct_hyperparam_str() -> str:
      return (
          f"mepochs{FLAGS.train_config.num_mini_epochs_per_epoch}"
          f"bstrain{FLAGS.train_config.batch_size_train}"
          f"bseval{FLAGS.train_config.batch_size_eval}"
          f"lr{FLAGS.train_config.learning_rate}"
          f"lrnepochsperdecay{FLAGS.train_config.learning_rate_num_epochs_per_decay}"
          f"lrdecayrate{FLAGS.train_config.learning_rate_decay_rate}"
          f"ad{FLAGS.train_config.average_decay}"
          f"ls{FLAGS.train_config.label_smoothing}"
          f"rho{FLAGS.train_config.rho}"
          f"mom{FLAGS.train_config.momentum}"
          f"eps{FLAGS.train_config.epsilon}"
          f"warmupmepochs{FLAGS.train_config.warmup_mini_epochs}"
      )
  
imports: import gc
  import os
  import re
  from typing import Any, Dict, Optional, Tuple
  import warnings
  from absl import app
  from absl import flags
  from absl import logging
  from ml_collections.config_flags import config_flags
  import tensorflow
  import tensorflow_addons
  from deepvariant import data_providers
  from deepvariant import dv_constants
  from deepvariant import dv_utils
  from deepvariant import keras_modeling
  from deepvariant.average_model_checkpoint_patched import AverageModelCheckpointPatched
  from third_party.nucleus.io import sharded_file_utils
  from official.modeling import optimization
label_desc: Use image node_id 4 for calling a global function with example usage: construct_hyperparam_str() and returns: str, FLAGS, str, FLAGS, str, FLAGS, str, FLAGS, str, FLAGS, str, FLAGS, str, FLAGS, str, FLAGS, str, FLAGS, str, FLAGS, str, FLAGS, str, FLAGS