class_docstr: An EfficientNet hypermodel.
  
  Models built by `HyperEfficientNet` take images with shape (height, width,
  channels) as input. The output are one-hot encoded with the length matching
  the number of classes specified by the `classes` argument.
  
  Args:
      input_shape: Optional shape tuple, e.g. `(256, 256, 3)`.  One of
          `input_shape` or `input_tensor` must be specified.
      input_tensor: Optional Keras tensor (i.e. output of `layers.Input()`)
          to use as image input for the model.  One of `input_shape` or
          `input_tensor` must be specified.
      classes: Optional number of classes to classify images into, only to be
          specified if `include_top` is True, and if no `weights` argument is
          specified.
      augmentation_model: Optional `Model` or `HyperModel` instance for image
          augmentation.
      **kwargs: Additional keyword arguments that apply to all hypermodels.
          See `keras_tuner.HyperModel`.
docstr: Compile model using hyperparameters in hp.
  
  When subclassing the hypermodel, this may be overridden to change
  behavior of compiling.
docstr_tok: ["Compile","model","using","hyperparameters","in","hp",".","When","subclassing","the","hypermodel",",","this","may","be","overridden","to","change","behavior","of","compiling","."]
code: def _compile(self, model, hp):
      """Compile model using hyperparameters in hp.
  
      When subclassing the hypermodel, this may be overridden to change
      behavior of compiling.
      """
      learning_rate = hp.Choice(
          "learning_rate", [0.1, 0.01, 0.001], default=0.01
      )
      optimizer = keras.optimizers.SGD(
          momentum=0.1, learning_rate=learning_rate
      )
  
      model.compile(
          optimizer=optimizer,
          loss="categorical_crossentropy",
          metrics=["accuracy"],
      )
  
file_path: keras-tuner/keras_tuner/applications/efficientnet.py
imports: from keras_tuner.api_export import keras_tuner_export
  from keras_tuner.backend import keras
  from keras_tuner.backend.keras import layers
  from keras_tuner.engine import hypermodel
label_desc: Use image node_id 3 for calling the HyperEfficientNet obj's underlying member method code with example usage: obj._compile(model, hp) without return types