class_docstr: A class to wrap training code.
docstr: TODO(brendan): We are always deriving based on the very first batch
  of validation data? This seems wrong...
code: def derive(self, sample_num=None, valid_idx=0):
      """TODO(brendan): We are always deriving based on the very first batch
      of validation data? This seems wrong...
      """
      hidden = self.shared.init_hidden(self.args.batch_size)
  
      if sample_num is None:
          sample_num = self.args.derive_num_sample
  
      dags, _, entropies = self.controller.sample(
          sample_num, with_details=True
      )
  
      max_R = 0
      best_dag = None
      for dag in dags:
          R, _ = self.get_reward(dag, entropies, hidden, valid_idx)
          if R.max() > max_R:
              max_R = R.max()
              best_dag = dag
  
      logger.info(f"derive | max_R: {max_R:8.6f}")
      fname = (
          f"{self.epoch:03d}-{self.controller_step:06d}-"
          f"{max_R:6.4f}-best.png"
      )
      path = os.path.join(self.args.model_dir, "networks", fname)
      utils.draw_network(best_dag, path)
      self.tb.image_summary("derive/best", [path], self.epoch)
  
      return best_dag
  
imports: import contextlib
  import glob
  import math
  import os
  import numpy
  import scipy.signal
  from tensorboard import TensorBoard
  import torch
  from torch import nn
  import torch.nn.parallel
  from torch.autograd import Variable
  import models
  import utils
label_desc: Use image node_id 9 for calling the Trainer obj's underlying member method code with example usage: obj.derive(sample_num, valid_idx) and returns: best_dag