class_docstr: A simple DNN subnetwork builder.
class_docstr_tok: ["A","simple","DNN","subnetwork","builder","."]
code: def __init__(
      self,
      name,
      feature_columns=None,
      learning_rate=0.01,
      layer_size=16,
      seed=13,
      use_tpu=False,
  ):
      self._name = name
      self._feature_columns = feature_columns
      self._learning_rate = learning_rate
      self._layer_size = layer_size
      self._seed = seed
      self._use_tpu = use_tpu
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import itertools
  import json
  import os
  from absl.testing import parameterized
  from adanet import tf_compat
  from adanet.core import testing_utils
  from adanet.core.tpu_estimator import TPUEstimator
  from adanet.subnetwork import Builder
  from adanet.subnetwork import Report
  from adanet.subnetwork import SimpleGenerator
  from adanet.subnetwork import Subnetwork
  import numpy
  import tensorflow.compat.v2
  from tensorflow_estimator.python.estimator.head import regression_head
label_desc: Use image node_id 1 to create a new _DNNBuilder object from inherited base classes: Builder with example: obj = _DNNBuilder(name, feature_columns, learning_rate, layer_size, seed, use_tpu)