code: def main(cfg):
      if hasattr(cfg.model.optim, "sched"):
          logging.warning(
              "You are using an optimizer scheduler while finetuning. Are you sure this is intended?"
          )
      if cfg.model.optim.lr > 1e-3 or cfg.model.optim.lr < 1e-5:
          logging.warning(
              "The recommended learning rate for finetuning is 2e-4"
          )
      trainer = pl.Trainer(**cfg.trainer)
      exp_manager(trainer, cfg.get("exp_manager", None))
      model = FastPitchModel(cfg=cfg.model, trainer=trainer)
      model.maybe_init_from_pretrained_checkpoint(cfg=cfg)
      lr_logger = pl.callbacks.LearningRateMonitor()
      epoch_time_logger = LogEpochTimeCallback()
      trainer.callbacks.extend([lr_logger, epoch_time_logger])
      trainer.fit(model)
  
imports: import pytorch_lightning
  from nemo.collections.common.callbacks import LogEpochTimeCallback
  from nemo.collections.tts.models import FastPitchModel
  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: main(cfg) without return types