class_docstr: Function caller to be used in the MLP experiments. 
class_docstr_tok: ["Function","caller","to","be","used","in","the","MLP","experiments","."]
docstr: Constructor. 
docstr_tok: ["Constructor","."]
code: def __init__(
      self,
      config,
      train_params,
      descr="",
      debug_mode=False,
      reporter="silent",
      tmp_dir="/tmp_cnn",
  ):
      """Constructor."""
      super(CNNFunctionCaller, self).__init__(
          config, train_params, descr, debug_mode, reporter
      )
      self.root_tmp_dir = tmp_dir
      self.data_file_str = self.train_params.data_dir
      # Check tf_params
      if not hasattr(self.train_params, "tf_params"):
          self.train_params.tf_params = get_default_cnn_tf_params()
  
file_path: dragonfly/examples/nas/cnn_function_caller.py
imports: from copy import deepcopy
  import os
  import shutil
  import tempfile
  from time import sleep
  from nn_function_caller import NNFunctionCaller
  from cg.cifar import run_tensorflow_cifar
label_desc: Use image node_id 1 to create a new CNNFunctionCaller object from inherited base classes: NNFunctionCaller with example: obj = CNNFunctionCaller(config, train_params, descr, debug_mode, reporter, tmp_dir)