class_docstr: An ensemble that linearly combines submodel outputs.
class_docstr_tok: ["An","ensemble","that","linearly","combines","submodel","outputs","."]
docstr: Initializes a WeightedEnsemble.
  
  Args:
      submodels: A list of `adanet.keras.SubModel` that compose the ensemble.
      output_units: The output size of the last layer of each submodel.
docstr_tok: ["Initializes","a","WeightedEnsemble",".","Args",":","submodels",":","A","list","of","`","adanet.keras.SubModel","`","that","compose","the","ensemble",".","output_units",":","The","output","size","of","the","last","layer","of","each","submodel","."]
code: def __init__(
      self, submodels: Sequence[tf.keras.Model], output_units: int
  ):
      """Initializes a WeightedEnsemble.
  
      Args:
          submodels: A list of `adanet.keras.SubModel` that compose the ensemble.
          output_units: The output size of the last layer of each submodel.
      """
  
      super().__init__(submodels)
      self.dense = tf.keras.layers.Dense(units=output_units)
  
file_path: adanet/adanet/experimental/keras/ensemble_model.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from typing import Sequence
  import tensorflow.compat.v2
label_desc: Use image node_id 1 to create a new WeightedEnsemble object from inherited base classes: EnsembleModel with example: obj = WeightedEnsemble(submodels, output_units)