class_docstr: An abstract ensembler.
class_docstr_tok: ["An","abstract","ensembler","."]
docstr: Returns an op for training an ensemble.
  
  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:
    ensemble: The :class:`adanet.ensemble.Ensemble` subclass instance returned
      by this instance's :meth:`build_ensemble`.
    loss: A :class:`tf.Tensor` containing the ensemble's loss to minimize.
    var_list: List of ensemble :class:`tf.Variable` parameters to update as
      part of the training operation.
    labels: Labels :class:`tf.Tensor` or a dictionary of string label name to
      :class:`tf.Tensor` (for multi-head).
    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 :code:`tf.summary` within this scope
      will use this :class:`adanet.Summary` under the hood.
    previous_ensemble: The best :class:`adanet.ensemble.Ensemble` from the
      previous iteration.
  Returns:
    Either a train op or an :class:`adanet.ensemble.TrainOpSpec`.
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code: def build_train_op(
      self,
      ensemble,
      loss,
      var_list,
      labels,
      iteration_step,
      summary,
      previous_ensemble,
  ):
      # pyformat: disable
      """Returns an op for training an ensemble.
  
      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:
        ensemble: The :class:`adanet.ensemble.Ensemble` subclass instance returned
          by this instance's :meth:`build_ensemble`.
        loss: A :class:`tf.Tensor` containing the ensemble's loss to minimize.
        var_list: List of ensemble :class:`tf.Variable` parameters to update as
          part of the training operation.
        labels: Labels :class:`tf.Tensor` or a dictionary of string label name to
          :class:`tf.Tensor` (for multi-head).
        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 :code:`tf.summary` within this scope
          will use this :class:`adanet.Summary` under the hood.
        previous_ensemble: The best :class:`adanet.ensemble.Ensemble` from the
          previous iteration.
      Returns:
        Either a train op or an :class:`adanet.ensemble.TrainOpSpec`.
      """
  
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 3 for calling the Ensembler obj's underlying member method code with example usage: obj.build_train_op(ensemble, loss, var_list, labels, iteration_step, summary, previous_ensemble) without return types