class_docstr: Builds a NASNet subnetwork for AdaNet.
docstr: Initializes a `Builder`.
  
  Args:
    feature_columns: The input feature columns of the problem.
    optimizer_fn: Function that accepts a float 'learning_rate' argument and
      returns an `Optimizer` instance and learning rate `Tensor` which may
      have a custom learning rate schedule applied.
    checkpoint_dir: Checkpoint directory.
    hparams: A `HParams` instance.
    seed: A Python integer. Used to create random seeds. See
      tf.set_random_seed for behavior.
  
  Returns:
    An instance of `Subnetwork`.
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code: def __init__(
      self, feature_columns, optimizer_fn, checkpoint_dir, hparams, seed
  ):
      """Initializes a `Builder`.
  
      Args:
        feature_columns: The input feature columns of the problem.
        optimizer_fn: Function that accepts a float 'learning_rate' argument and
          returns an `Optimizer` instance and learning rate `Tensor` which may
          have a custom learning rate schedule applied.
        checkpoint_dir: Checkpoint directory.
        hparams: A `HParams` instance.
        seed: A Python integer. Used to create random seeds. See
          tf.set_random_seed for behavior.
  
      Returns:
        An instance of `Subnetwork`.
      """
  
      self._feature_columns = feature_columns
      self._optimizer_fn = optimizer_fn
      self._checkpoint_dir = checkpoint_dir
      self._hparams = hparams
  
      self._aux_head_weight = hparams.aux_head_weight
      self._learn_mixture_weights = hparams.learn_mixture_weights
      self._initial_learning_rate = hparams.initial_learning_rate
      self._knowledge_distillation = hparams.knowledge_distillation
      self._label_smoothing = hparams.label_smoothing
      self._model_version = hparams.model_version
      self._weight_decay = hparams.weight_decay
      # `num_cells` and `num_conv_filters` are not directly used here. They are
      # passed inside hparams to build_nasnet function. They are just saved in
      # `shared`.
      self._num_cells = hparams.num_cells
      self._num_conv_filters = hparams.num_conv_filters
      self._seed = seed
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  import adanet
  import tensorflow.compat.v1
label_desc: Use image node_id 1 to create a new Builder object from inherited base classes: adanet.subnetwork with example: obj = Builder(feature_columns, optimizer_fn, checkpoint_dir, hparams, seed)