class_docstr: Generates a list of `Builders`.
docstr: Generator that gradually grows the architecture.
  
  In each iteration, we generate one deeper candidate and one wider candidate.
  
  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.
    iteration_steps: The number of train steps in per iteration. Required for
      ScheduleDropPath algorithm.
    checkpoint_dir: Checkpoint directory.
    hparams: Hyper-parameters.
    seed: A Python integer. Used to create random seeds. See
      tf.set_random_seed for behavior.
  
  Returns:
    An instance of `Generator`.
  
  Raises:
    ValueError: If num_cells is not divisible by 3.
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code: def __init__(
      self,
      feature_columns,
      optimizer_fn,
      iteration_steps,
      checkpoint_dir,
      hparams,
      seed=None,
  ):
      """Generator that gradually grows the architecture.
  
      In each iteration, we generate one deeper candidate and one wider candidate.
  
      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.
        iteration_steps: The number of train steps in per iteration. Required for
          ScheduleDropPath algorithm.
        checkpoint_dir: Checkpoint directory.
        hparams: Hyper-parameters.
        seed: A Python integer. Used to create random seeds. See
          tf.set_random_seed for behavior.
  
      Returns:
        An instance of `Generator`.
  
      Raises:
        ValueError: If num_cells is not divisible by 3.
      """
  
      if hparams.num_cells % 3 != 0:
          raise ValueError("num_cells must be a multiple of 3.")
  
      self._hparams = hparams
      self._builder_fn = functools.partial(
          Builder,
          feature_columns=feature_columns,
          optimizer_fn=optimizer_fn,
          checkpoint_dir=checkpoint_dir,
          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 DynamicGenerator object from inherited base classes: adanet.subnetwork with example: obj = DynamicGenerator(feature_columns, optimizer_fn, iteration_steps, checkpoint_dir, hparams, seed)