class_docstr: Trains an instance of the Model class.
docstr: Evaluate the child model.
  
  Args:
    model: image model that will be evaluated.
    data_loader: dataset object to extract eval data from.
    mode: will the model be evalled on train, val or test.
  
  Returns:
    Accuracy of the model on the specified dataset.
docstr_tok: ["Evaluate","the","child","model",".","Args",":","model",":","image","model","that","will","be","evaluated",".","data_loader",":","dataset","object","to","extract","eval","data","from",".","mode",":","will","the","model","be","evalled","on","train",",","val","or","test",".","Returns",":","Accuracy","of","the","model","on","the","specified","dataset","."]
code: def eval_child_model(self, mode: str, pass_id: int = 0) -> dict:
      """Evaluate the child model.
  
      Args:
        model: image model that will be evaluated.
        data_loader: dataset object to extract eval data from.
        mode: will the model be evalled on train, val or test.
  
      Returns:
        Accuracy of the model on the specified dataset.
      """
      eval_loader = self.trainer.eval_loader
      res = self.trainer.evaluate_process(eval_loader)
      top1_acc = res["metrics"]["acc"]
  
      if mode == "val":
          return {"val_acc": top1_acc}
      elif mode == "test":
          return {"test_acc": top1_acc}
      else:
          raise NotImplementedError
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import time
  import six
  import os
  from typing import Dict, List, Optional, Union, Tuple
  from auto_augment.autoaug.utils import log
  import logging
  import auto_augment
label_desc: Use image node_id 7 for calling the HubFitterClassifer obj's underlying member method code with example usage: obj.eval_child_model(mode, pass_id) and returns: dict, dict