class_docstr: An abstract ensembler.
class_docstr_tok: ["An","abstract","ensembler","."]
docstr: Builds an ensemble of subnetworks.
  
  Accessing the global step via :meth:`tf.train.get_or_create_global_step()`
  or :meth:`tf.train.get_global_step()` within this scope will return an
  incrementable iteration step since the beginning of the iteration.
  
  Args:
    subnetworks: Ordered iterable of :class:`adanet.subnetwork.Subnetwork`
      instances to ensemble. Must have at least one element.
    previous_ensemble_subnetworks: Ordered iterable of
      :class:`adanet.subnetwork.Subnetwork` instances present in previous
      ensemble to be used. The subnetworks from previous_ensemble not
      included in this list should be pruned. Can be set to None or empty.
    features: Input :code:`dict` of :class:`tf.Tensor` objects.
    labels: Labels :class:`tf.Tensor` or a dictionary of string label name to
      :class:`tf.Tensor` (for multi-head). Can be :code:`None`.
    logits_dimension: Size of the last dimension of the logits
      :class:`tf.Tensor`. Typically, logits have for shape `[batch_size,
      logits_dimension]`.
    training: A python boolean indicating whether the graph is in training
      mode or prediction mode.
    iteration_step: Integer :class:`tf.Tensor` representing the step since the
      beginning of the current iteration, as opposed to the global step.
    summary: An :class:`adanet.Summary` for scoping summaries to individual
      ensembles in Tensorboard. Using :meth:`tf.summary` within this scope
      will use this :class:`adanet.Summary` under the hood.
    previous_ensemble: The best :class:`adanet.Ensemble` from iteration *t-1*.
      The created subnetwork will extend the previous ensemble to form the
      :class:`adanet.Ensemble` at iteration *t*.
    previous_iteration_checkpoint: The `tf.train.Checkpoint` object associated
      with the previous iteration.
  
  Returns:
    An :class:`adanet.ensemble.Ensemble` subclass instance.
docstr_tok: ["Builds","an","ensemble","of","subnetworks",".","Accessing","the","global","step","via",":","meth",":","`","tf.train.get_or_create_global_step","(",")","`","or",":","meth",":","`","tf.train.get_global_step","(",")","`","within","this","scope","will","return","an","incrementable","iteration","step","since","the","beginning","of","the","iteration",".","Args",":","subnetworks",":","Ordered","iterable","of",":","class",":","`","adanet.subnetwork.Subnetwork","`","instances","to","ensemble",".","Must","have","at","least","one","element",".","previous_ensemble_subnetworks",":","Ordered","iterable","of",":","class",":","`","adanet.subnetwork.Subnetwork","`","instances","present","in","previous","ensemble","to","be","used",".","The","subnetworks","from","previous_ensemble","not","included","in","this","list","should","be","pruned",".","Can","be","set","to","None","or","empty",".","features",":","Input",":","code",":","`","dict","`","of",":","class",":","`","tf.Tensor","`","objects",".","labels",":","Labels",":","class",":","`","tf.Tensor","`","or","a","dictionary","of","string","label","name","to",":","class",":","`","tf.Tensor","`","(","for","multi-head",")",".","Can","be",":","code",":","`","None","`",".","logits_dimension",":","Size","of","the","last","dimension","of","the","logits",":","class",":","`","tf.Tensor","`",".","Typically",",","logits","have","for","shape","`","[","batch_size",",","logits_dimension","]","`",".","training",":","A","python","boolean","indicating","whether","the","graph","is","in","training","mode","or","prediction","mode",".","iteration_step",":","Integer",":","class",":","`","tf.Tensor","`","representing","the","step","since","the","beginning","of","the","current","iteration",",","as","opposed","to","the","global","step",".","summary",":","An",":","class",":","`","adanet.Summary","`","for","scoping","summaries","to","individual","ensembles","in","Tensorboard",".","Using",":","meth",":","`","tf.summary","`","within","this","scope","will","use","this",":","class",":","`","adanet.Summary","`","under","the","hood",".","previous_ensemble",":","The","best",":","class",":","`","adanet.Ensemble","`","from","iteration","*","t-1","*",".","The","created","subnetwork","will","extend","the","previous","ensemble","to","form","the",":","class",":","`","adanet.Ensemble","`","at","iteration","*","t","*",".","previous_iteration_checkpoint",":","The","`","tf.train.Checkpoint","`","object","associated","with","the","previous","iteration",".","Returns",":","An",":","class",":","`","adanet.ensemble.Ensemble","`","subclass","instance","."]
code: def build_ensemble(
      self,
      subnetworks,
      previous_ensemble_subnetworks,
      features,
      labels,
      logits_dimension,
      training,
      iteration_step,
      summary,
      previous_ensemble,
      previous_iteration_checkpoint,
  ):
      # pyformat: disable
      """Builds an ensemble of subnetworks.
  
      Accessing the global step via :meth:`tf.train.get_or_create_global_step()`
      or :meth:`tf.train.get_global_step()` within this scope will return an
      incrementable iteration step since the beginning of the iteration.
  
      Args:
        subnetworks: Ordered iterable of :class:`adanet.subnetwork.Subnetwork`
          instances to ensemble. Must have at least one element.
        previous_ensemble_subnetworks: Ordered iterable of
          :class:`adanet.subnetwork.Subnetwork` instances present in previous
          ensemble to be used. The subnetworks from previous_ensemble not
          included in this list should be pruned. Can be set to None or empty.
        features: Input :code:`dict` of :class:`tf.Tensor` objects.
        labels: Labels :class:`tf.Tensor` or a dictionary of string label name to
          :class:`tf.Tensor` (for multi-head). Can be :code:`None`.
        logits_dimension: Size of the last dimension of the logits
          :class:`tf.Tensor`. Typically, logits have for shape `[batch_size,
          logits_dimension]`.
        training: A python boolean indicating whether the graph is in training
          mode or prediction mode.
        iteration_step: Integer :class:`tf.Tensor` representing the step since the
          beginning of the current iteration, as opposed to the global step.
        summary: An :class:`adanet.Summary` for scoping summaries to individual
          ensembles in Tensorboard. Using :meth:`tf.summary` within this scope
          will use this :class:`adanet.Summary` under the hood.
        previous_ensemble: The best :class:`adanet.Ensemble` from iteration *t-1*.
          The created subnetwork will extend the previous ensemble to form the
          :class:`adanet.Ensemble` at iteration *t*.
        previous_iteration_checkpoint: The `tf.train.Checkpoint` object associated
          with the previous iteration.
  
      Returns:
        An :class:`adanet.ensemble.Ensemble` subclass instance.
      """
  
file_path: adanet/adanet/ensemble/ensembler.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import abc
  import collections
  import six
label_desc: Use image node_id 2 for calling the Ensembler obj's underlying member method code with example usage: obj.build_ensemble(subnetworks, previous_ensemble_subnetworks, features, labels, logits_dimension, training, iteration_step, summary, previous_ensemble, previous_iteration_checkpoint) without return types