class_docstr: An ensemble of Keras models.
class_docstr_tok: ["An","ensemble","of","Keras","models","."]
docstr: Initializes an EnsembleModel.
  
  Args:
    submodels: A list of `tf.keras.Model` that compose the ensemble.
    freeze_submodels: Whether to freeze the weights of submodels.
docstr_tok: ["Initializes","an","EnsembleModel",".","Args",":","submodels",":","A","list","of","`","tf.keras.Model","`","that","compose","the","ensemble",".","freeze_submodels",":","Whether","to","freeze","the","weights","of","submodels","."]
code: def __init__(
      self,
      submodels: Sequence[tf.keras.Model],
      freeze_submodels: bool = True,
  ):
      """Initializes an EnsembleModel.
  
      Args:
        submodels: A list of `tf.keras.Model` that compose the ensemble.
        freeze_submodels: Whether to freeze the weights of submodels.
      """
  
      super().__init__()
      if freeze_submodels:
          for submodel in submodels:
              for layer in submodel.layers:
                  layer.trainable = False
      self._submodels = submodels
  
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 EnsembleModel object from inherited base classes: tf.keras with example: obj = EnsembleModel(submodels, freeze_submodels)