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`.
code: def __init__(
      self,
      input_shape=None,
      input_tensor=None,
      classes=None,
      augmentation_model=None,
      **kwargs,
  ):
      if not isinstance(
          augmentation_model,
          (hypermodel.HyperModel, keras.Model, type(None)),
      ):
          raise ValueError(
              "Keyword augmentation_model should be "
              "a `HyperModel`, a Keras `Model` or "
              f"empty. Received {augmentation_model}."
          )
  
      if not classes:
          raise ValueError("You must specify `classes`.")
  
      if input_shape is None and input_tensor is None:
          raise ValueError(
              "You must specify either `input_shape` or `input_tensor`."
          )
  
      self.input_shape = input_shape
      self.input_tensor = input_tensor
      self.classes = classes
      self.augmentation_model = augmentation_model
  
      super().__init__(**kwargs)
  
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 1 to create a new HyperEfficientNet object from inherited base classes: hypermodel with example: obj = HyperEfficientNet(input_shape, input_tensor, classes, augmentation_model)