class_docstr: Handles the main model training procedure. This includes performing evaluation on the dev set at regular
  intervals during training as well as evaluation on the test set at the end of training.
class_docstr_tok: ["Handles","the","main","model","training","procedure",".","This","includes","performing","evaluation","on","the","dev","set","at","regular","intervals","during","training","as","well","as","evaluation","on","the","test","set","at","the","end","of","training","."]
code: def log_params(self):
      params = {
          "epochs": self.epochs,
          "n_gpu": self.n_gpu,
          "device": self.device,
      }
      MlLogger.log_params(params)
  
imports: import logging
  import sys
  import torch
  from pathlib import Path
  from tqdm import tqdm
  import numpy
  import shutil
  import dill
  from farm.utils import MLFlowLogger
  from farm.utils import GracefulKiller, set_all_seeds
  from farm.eval import Evaluator
  from farm.data_handler.data_silo import DataSilo
  from farm.visual.ascii.images import GROWING_TREE
  from farm.modeling.adaptive_model import AdaptiveModel
  from farm.modeling.biadaptive_model import BiAdaptiveModel
  from farm.modeling.optimization import get_scheduler
label_desc: Use image node_id 5 for calling the Trainer obj's underlying member method code with example usage: obj.log_params() without return types