code: def get_trainer(trainer_name: str) -> Any:
      """Gets the trainer matching the given name."""
      if trainer_name == "duplex_vit_classification_trainer":
          return duplex_vit_classification_trainer.train
      elif trainer_name == "classification_trainer":
          return tasseo_classification_trainer.train
      elif trainer_name == "transfer_trainer":
          return transfer_trainer.train
      elif trainer_name == "inference":
          return inference.evaluate
      else:
          return trainers.get_trainer(trainer_name)
  
imports: from typing import Any
  from absl import flags
  from clu import metric_writers
  import jax
  import jax.numpy
  import ml_collections
  from scenic import app
  from scenic.projects.tasseo import classification_trainer
  from scenic.projects.tasseo import duplex_vit
  from scenic.projects.tasseo import duplex_vit_classification_trainer
  from scenic.projects.tasseo import inference
  from scenic.projects.tasseo import transfer_trainer
  from scenic.projects.tasseo import vit
  from scenic.projects.tasseo import xvit
  from scenic.projects.tasseo.datasets import abnormality_baseline_dataset
  from scenic.projects.tasseo.datasets import chrmID_baseline_dataset
  from scenic.projects.tasseo.datasets import chrmID_big_metaphase_context_dataset
  from scenic.projects.tasseo.datasets import chrmID_dataset
  from scenic.projects.tasseo.datasets import chrmID_metaphase_context_dataset
  from scenic.projects.tasseo.datasets import longtail_baseline_dataset
  from scenic.projects.tasseo.datasets import longtail_rhs_baseline_dataset
  from scenic.projects.tasseo.datasets import metaphase_sexid_dataset
  from scenic.train_lib import train_utils
  from scenic.train_lib import trainers
label_desc: Use image node_id 2 for calling a global function with example usage: get_trainer(trainer_name) and returns: duplex_vit_classification_trainer, tasseo_classification_trainer, transfer_trainer, inference, trainers