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.megatron_amp_O2 = cfg.model.get(
              "megatron_amp_O2", False
          )
          if hasattr(t5_cfg, "encoder") and hasattr(t5_cfg, "decoder"):
              t5_cfg.encoder.masked_softmax_fusion = False
              t5_cfg.decoder.masked_softmax_fusion = False
              t5_cfg.encoder.hidden_dropout = cfg.model.get(
                  "hidden_dropout", 0.1
              )
              t5_cfg.decoder.hidden_dropout = cfg.model.get(
                  "hidden_dropout", 0.1
              )
              if hasattr(t5_cfg.encoder, "ffn_dropout"):
                  t5_cfg.encoder.ffn_dropout = cfg.model.get(
                      "ffn_dropout", 0.1
                  )
              if hasattr(t5_cfg.decoder, "ffn_dropout"):
                  t5_cfg.decoder.ffn_dropout = cfg.model.get(
                      "ffn_dropout", 0.1
                  )
  
              if hasattr(cfg.model, "encoder"):
                  if hasattr(
                      cfg.model.encoder, "position_embedding_type"
                  ):
                      t5_cfg.encoder.position_embedding_type = (
                          cfg.model.encoder.position_embedding_type
                      )
                  if hasattr(cfg.model.encoder, "use_flash_attention"):
                      t5_cfg.encoder.use_flash_attention = (
                          cfg.model.encoder.use_flash_attention
                      )
                  if hasattr(cfg.model.encoder, "attention_dropout"):
                      t5_cfg.encoder.attention_dropout = (
                          cfg.model.encoder.attention_dropout
                      )
              if hasattr(cfg.model, "decoder"):
                  if hasattr(
                      cfg.model.decoder, "position_embedding_type"
                  ):
                      t5_cfg.decoder.position_embedding_type = (
                          cfg.model.decoder.position_embedding_type
                      )
                  if hasattr(cfg.model.decoder, "use_flash_attention"):
                      t5_cfg.decoder.use_flash_attention = (
                          cfg.model.decoder.use_flash_attention
                      )
                  if hasattr(cfg.model.decoder, "attention_dropout"):
                      t5_cfg.decoder.attention_dropout = (
                          cfg.model.decoder.attention_dropout
                      )
          else:
              t5_cfg.hidden_dropout = cfg.model.get(
                  "hidden_dropout", 0.1
              )
              t5_cfg.attention_dropout = cfg.model.get(
                  "attention_dropout", 0.1
              )
              t5_cfg.masked_softmax_fusion = False
          t5_cfg.data = cfg.model.data
          t5_cfg.precision = cfg.trainer.precision
          t5_cfg.optim = cfg.model.optim
          t5_cfg.micro_batch_size = (
              cfg.model.data.train_ds.micro_batch_size
          )
          t5_cfg.global_batch_size = (
              cfg.model.data.train_ds.global_batch_size
          )
          # XNLI has eval languages in the yaml config.
          if hasattr(cfg.model, "eval_languages"):
              t5_cfg.eval_languages = cfg.model.eval_languages
  
          # 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
  
imports: import os
  import tempfile
  import torch.multiprocessing
  from omegaconf.omegaconf import OmegaConf, open_dict
  from pytorch_lightning import Trainer
  from pytorch_lightning.plugins.environments import TorchElasticEnvironment
  from pytorch_lightning.trainer.connectors.checkpoint_connector import _CheckpointConnector
  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.modules.common.megatron.megatron_init import fake_initialize_model_parallel
  from nemo.collections.nlp.parts.nlp_overrides import CustomProgressBar, GradScaler, MegatronHalfPrecisionPlugin, NLPDDPStrategy, NLPSaveRestoreConnector, PipelineMixedPrecisionPlugin
  from nemo.core.config import hydra_runner
  from nemo.utils import AppState, logging
  from nemo.utils.exp_manager import exp_manager
  from nemo.utils.model_utils import inject_model_parallel_rank
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