code: def _modify_config(t5_cfg, cfg, add_cfg_to_tree=False):
      """
      This function modifies the original t5 pre-training config (t5_cfg) with attributes from the finetuning config (cfg).
      The `add_cfg_to_tree` arg adds `cfg` to the top of the yaml tree which is needed for all `hparams.yaml` files when passed as an arg to `load_from_checkpoint()`.
      """
      OmegaConf.set_struct(t5_cfg, True)
      with open_dict(t5_cfg):
          t5_cfg.precision = cfg.trainer.precision
          # Overwrite data configs
          if (
              cfg.model.data.validation_ds.get("src_file_name", None)
              is not None
          ):
              logging.info(
                  "Found validation_ds.src_file_name in the config file. Overriding the finetuned model config file with the values from the new config file."
              )
              t5_cfg.data.validation_ds.src_file_name = (
                  cfg.model.data.validation_ds.src_file_name
              )
          if (
              cfg.model.data.validation_ds.get("tgt_file_name", None)
              is not None
          ):
              logging.info(
                  "Found validation_ds.tgt_file_name in the config file. Overriding the finetuned model config file with the values from the new config file."
              )
              t5_cfg.data.validation_ds.tgt_file_name = (
                  cfg.model.data.validation_ds.tgt_file_name
              )
  
          if (
              "write_predictions_to_file"
              in cfg.model.data.validation_ds
          ):
              t5_cfg.data.validation_ds.write_predictions_to_file = (
                  cfg.model.data.validation_ds.write_predictions_to_file
              )
          if "output_file_path_prefix" in cfg.model.data.validation_ds:
              t5_cfg.data.validation_ds.output_file_path_prefix = (
                  cfg.model.data.validation_ds.output_file_path_prefix
              )
  
          t5_cfg.data.validation_ds.micro_batch_size = (
              cfg.model.data.validation_ds.micro_batch_size
          )
          t5_cfg.data.validation_ds.global_batch_size = (
              cfg.model.data.validation_ds.global_batch_size
          )
  
          # This is needed when modifying a hparam file directly to load `.ckpt` files.
          # This is not needed to modify the cfg in `.nemo` files.
          if add_cfg_to_tree:
              OmegaConf.resolve(t5_cfg)
              t5_cfg.cfg = t5_cfg
  
      return t5_cfg
  
file_path: NeMo/examples/nlp/language_modeling/megatron_t5_seq2seq_eval.py
imports: from megatron_t5_seq2seq_finetune import load_from_checkpoint_dir, load_from_nemo, validate_checkpoint_loading_args
  from omegaconf.omegaconf import OmegaConf, open_dict
  from pytorch_lightning import Trainer
  from pytorch_lightning.plugins.environments import TorchElasticEnvironment
  from pytorch_lightning.plugins.precision import MixedPrecisionPlugin
  from nemo.collections.nlp.models.language_modeling.megatron_glue_model import MegatronT5GLUEModel
  from nemo.collections.nlp.models.language_modeling.megatron_t0_model import MegatronT0Model
  from nemo.collections.nlp.models.language_modeling.megatron_t5_sft_model import MegatronT5SFTModel
  from nemo.collections.nlp.parts.nlp_overrides import GradScaler, MegatronHalfPrecisionPlugin, NLPDDPStrategy
  from nemo.core.config import hydra_runner
  from nemo.utils import logging
  from nemo.utils.exp_manager import exp_manager
label_desc: Use image node_id 1 for calling a global function with example usage: _modify_config(t5_cfg, cfg, add_cfg_to_tree) and returns: t5_cfg