code: def set_to_train(self):
      for T in self.trainable_attributes():
          for k, v in T.items():
              if k in self.freeze_these:
                  c_f.set_requires_grad(v, requires_grad=False)
                  v.eval()
              else:
                  v.train()
      self.maybe_freeze_trunk_batchnorm()
  
imports: import torch
  import tqdm
  from ..utils import common_functions
  from ..utils import loss_tracker
  from ..utils.key_checker import KeyChecker, KeyCheckerDict
label_desc: Use image node_id 28 for calling the BaseTrainer obj's underlying member method code with example usage: obj.set_to_train() without return types