class_docstr: A preprocessing layer which randomly zooms images during training.
  
  This layer will randomly zoom in or out on each axis of an image
  independently, filling empty space according to `fill_mode`.
  
  Input pixel values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and
  of integer or floating point dtype.
  By default, the layer will output floats.
  
  Input shape:
      3D (unbatched) or 4D (batched) tensor with shape:
      `(..., height, width, channels)`, in `"channels_last"` format,
      or `(..., channels, height, width)`, in `"channels_first"` format.
  
  Output shape:
      3D (unbatched) or 4D (batched) tensor with shape:
      `(..., target_height, target_width, channels)`,
      or `(..., channels, target_height, target_width)`,
      in `"channels_first"` format.
  
  **Note:** This layer is safe to use inside a `tf.data` pipeline
  (independently of which backend you're using).
  
  Args:
      height_factor: a float represented as fraction of value, or a tuple of
          size 2 representing lower and upper bound for zooming vertically.
          When represented as a single float, this value is used for both the
          upper and lower bound. A positive value means zooming out, while a
          negative value means zooming in. For instance,
          `height_factor=(0.2, 0.3)` result in an output zoomed out by a
          random amount in the range `[+20%, +30%]`.
          `height_factor=(-0.3, -0.2)` result in an output zoomed in by a
          random amount in the range `[+20%, +30%]`.
      width_factor: a float represented as fraction of value, or a tuple of
          size 2 representing lower and upper bound for zooming horizontally.
          When represented as a single float, this value is used for both the
          upper and lower bound. For instance, `width_factor=(0.2, 0.3)`
          result in an output zooming out between 20% to 30%.
          `width_factor=(-0.3, -0.2)` result in an output zooming in between
          20% to 30%. `None` means i.e., zooming vertical and horizontal
          directions by preserving the aspect ratio. Defaults to `None`.
      fill_mode: Points outside the boundaries of the input are filled
          according to the given mode. Available methods are `"constant"`,
          `"nearest"`, `"wrap"` and `"reflect"`. Defaults to `"constant"`.
          - `"reflect"`: `(d c b a | a b c d | d c b a)`
              The input is extended by reflecting about the edge of the last
              pixel.
          - `"constant"`: `(k k k k | a b c d | k k k k)`
              The input is extended by filling all values beyond
              the edge with the same constant value k specified by
              `fill_value`.
          - `"wrap"`: `(a b c d | a b c d | a b c d)`
              The input is extended by wrapping around to the opposite edge.
          - `"nearest"`: `(a a a a | a b c d | d d d d)`
              The input is extended by the nearest pixel.
          Note that when using torch backend, `"reflect"` is redirected to
          `"mirror"` `(c d c b | a b c d | c b a b)` because torch does not
          support `"reflect"`.
          Note that torch backend does not support `"wrap"`.
      interpolation: Interpolation mode. Supported values: `"nearest"`,
          `"bilinear"`.
      seed: Integer. Used to create a random seed.
      fill_value: a float represents the value to be filled outside
          the boundaries when `fill_mode="constant"`.
      data_format: string, either `"channels_last"` or `"channels_first"`.
          The ordering of the dimensions in the inputs. `"channels_last"`
          corresponds to inputs with shape `(batch, height, width, channels)`
          while `"channels_first"` corresponds to inputs with shape
          `(batch, channels, height, width)`. It defaults to the
          `image_data_format` value found in your Keras config file at
          `~/.keras/keras.json`. If you never set it, then it will be
          `"channels_last"`.
      **kwargs: Base layer keyword arguments, such as `name` and `dtype`.
  
  Example:
  
  >>> input_img = np.random.random((32, 224, 224, 3))
  >>> layer = keras.layers.RandomZoom(.5, .2)
  >>> out_img = layer(input_img)
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code: def get_config(self):
      base_config = super().get_config()
      config = {
          "height_factor": self.height_factor,
          "width_factor": self.width_factor,
          "fill_mode": self.fill_mode,
          "interpolation": self.interpolation,
          "seed": self.seed,
          "fill_value": self.fill_value,
          "data_format": self.data_format,
      }
      return {**base_config, **config}
  
file_path: keras/keras/layers/preprocessing/random_zoom.py
imports: from keras import backend
  from keras.api_export import keras_export
  from keras.layers.preprocessing.tf_data_layer import TFDataLayer
  from keras.random.seed_generator import SeedGenerator
label_desc: Use image node_id 8 for calling the RandomZoom obj's underlying member method code with example usage: obj.get_config() and returns: dict