class_docstr: Use neural networks as an alternative to GPs to model distributions over functions in bayesian optimization.
  
  Parameters
  ----------
  optimize : maximize | minimize, default = maximize
      If 'maximize', the tuner will target to maximize metrics. If 'minimize', the tuner will target to minimize metrics.
  sample_size : int, default = 1000
      Number of samples to select in each iteration. The best one will be picked from the samples as the next trial.
  trials_per_update : int, default = 20
      Number of trials to collect before updating the model.
  num_epochs_per_training : int, default = 500
      Number of epochs to train DNGO model.
code: def import_data(self, data):
      for d in data:
          self.x.append(d["parameter"])
          self.y.append(self._get_default_value(d["value"]))
      self._update_model()
  
imports: import logging
  import warnings
  import numpy
  import torch
  from pybnn import DNGO
  from torch.distributions import Normal
  import nni.parameter_expressions
  from nni import ClassArgsValidator
  from nni.common.hpo_utils import validate_search_space
  from nni.tuner import Tuner
label_desc: Use image node_id 5 for calling the DNGOTuner obj's underlying member method code with example usage: obj.import_data(data) without return types