class_docstr: 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_mlp",
  ):
      """Constructor."""
      super(MLPFunctionCaller, self).__init__(
          config, train_params, descr, debug_mode, reporter
      )
      self.root_tmp_dir = tmp_dir
      # Load data
      with open(self.train_params.data_train_file, "rb") as input_file:
          if sys.version_info[0] < 3:
              data = pic.load(input_file)
          else:
              data = pic.load(input_file, encoding="latin1")
      self.data_train = data["train"]
      self.data_vali = data["vali"]
      self.reporter.writeln(
          "Loaded data: " + self.train_params.data_train_file
      )
      self.reporter.writeln(
          "Training data shape: "
          + "x: "
          + str(self.data_train["x"].shape)
          + ", "
          + "y: "
          + str(self.data_train["y"].shape)
      )
      self.reporter.writeln(
          "Validation data shape: "
          + "x: "
          + str(self.data_vali["x"].shape)
          + ", "
          + "y: "
          + str(self.data_vali["y"].shape)
      )
      # Check tf_params
      if not hasattr(self.train_params, "tf_params"):
          self.train_params.tf_params = get_default_mlp_tf_params()
  
file_path: dragonfly/examples/nas/mlp_function_caller.py
imports: import sys
  from copy import deepcopy
  import os
  import shutil
  import tempfile
  from time import sleep
  from nn_function_caller import NNFunctionCaller
  from cg import run_tensorflow
label_desc: Use image node_id 1 to create a new MLPFunctionCaller object from inherited base classes: NNFunctionCaller with example: obj = MLPFunctionCaller(config, train_params, descr, debug_mode, reporter, tmp_dir)