def __init__(
        self,
        nb_samples: int,
        clip_values: tuple[float, float],
        zoom: float | tuple[float, float],
        apply_fit: bool = False,
        apply_predict: bool = True,
    ) -> None:
        """
        Create an instance of EoTZoomBlurTensorFlow.

        :param nb_samples: Number of random samples per input sample.
        :param clip_values: Tuple of float representing minimum and maximum values of input `(min, max)`.
        :param zoom: Range to sample the zoom factor. A single float is translated to range [1.0, zoom] or a tuple of
                     floats is used to create sampling range [zoom[0], zoom[1]]. The applied zoom is sampled uniformly
                     from this range for each image.
        :param apply_fit: True if applied during fitting/training.
        :param apply_predict: True if applied during predicting.
        """
        super().__init__(
            apply_fit=apply_fit, apply_predict=apply_predict, nb_samples=nb_samples, clip_values=clip_values
        )

        self.zoom = zoom
        self.zoom_range = (1.0, zoom) if isinstance(zoom, (int, float)) else zoom
        self._check_params()

        Create an instance of EoTZoomBlurTensorFlow.

        :param nb_samples: Number of random samples per input sample.
        :param clip_values: Tuple of float representing minimum and maximum values of input `(min, max)`.
        :param zoom: Range to sample the zoom factor. A single float is translated to range [1.0, zoom] or a tuple of
                     floats is used to create sampling range [zoom[0], zoom[1]]. The applied zoom is sampled uniformly
                     from this range for each image.
        :param apply_fit: True if applied during fitting/training.
        :param apply_predict: True if applied during predicting.
        
art/preprocessing/expectation_over_transformation/natural_corruptions/zoom_blur/tensorflow.py