def affine_transform(
    images,
    transform,
    interpolation="bilinear",
    fill_mode="constant",
    fill_value=0,
    data_format=None,
):
    """Applies the given transform(s) to the image(s).

    Args:
        images: Input image or batch of images. Must be 3D or 4D.
        transform: Projective transform matrix/matrices. A vector of length 8 or
            tensor of size N x 8. If one row of transform is
            `[a0, a1, a2, b0, b1, b2, c0, c1]`, then it maps the output point
            `(x, y)` to a transformed input point
            `(x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k)`,
            where `k = c0 x + c1 y + 1`. The transform is inverted compared to
            the transform mapping input points to output points. Note that
            gradients are not backpropagated into transformation parameters.
            Note that `c0` and `c1` are only effective when using TensorFlow
            backend and will be considered as `0` when using other backends.
        interpolation: Interpolation method. Available methods are `"nearest"`,
            and `"bilinear"`. Defaults to `"bilinear"`.
        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.
        fill_value: Value used for points outside the boundaries of the input if
            `fill_mode="constant"`. Defaults to `0`.
        data_format: A string specifying the data format of the input tensor.
            It can be either `"channels_last"` or `"channels_first"`.
            `"channels_last"` corresponds to inputs with shape
            `(batch, height, width, channels)`, while `"channels_first"`
            corresponds to inputs with shape `(batch, channels, height, width)`.
            If not specified, the value will default to
            `keras.config.image_data_format`.

    Returns:
        Applied affine transform image or batch of images.

    Examples:

    >>> x = np.random.random((2, 64, 80, 3)) # batch of 2 RGB images
    >>> transform = np.array(
    ...     [
    ...         [1.5, 0, -20, 0, 1.5, -16, 0, 0],  # zoom
    ...         [1, 0, -20, 0, 1, -16, 0, 0],  # translation
    ...     ]
    ... )
    >>> y = keras.ops.image.affine_transform(x, transform)
    >>> y.shape
    (2, 64, 80, 3)

    >>> x = np.random.random((64, 80, 3)) # single RGB image
    >>> transform = np.array([1.0, 0.5, -20, 0.5, 1.0, -16, 0, 0])  # shear
    >>> y = keras.ops.image.affine_transform(x, transform)
    >>> y.shape
    (64, 80, 3)

    >>> x = np.random.random((2, 3, 64, 80)) # batch of 2 RGB images
    >>> transform = np.array(
    ...     [
    ...         [1.5, 0, -20, 0, 1.5, -16, 0, 0],  # zoom
    ...         [1, 0, -20, 0, 1, -16, 0, 0],  # translation
    ...     ]
    ... )
    >>> y = keras.ops.image.affine_transform(x, transform,
    ...     data_format="channels_first")
    >>> y.shape
    (2, 3, 64, 80)
    """
    if any_symbolic_tensors((images, transform)):
        return AffineTransform(
            interpolation=interpolation,
            fill_mode=fill_mode,
            fill_value=fill_value,
            data_format=data_format,
        ).symbolic_call(images, transform)
    return backend.image.affine_transform(
        images,
        transform,
        interpolation=interpolation,
        fill_mode=fill_mode,
        fill_value=fill_value,
        data_format=data_format,
    )
Applies the given transform(s) to the image(s).

    Args:
        images: Input image or batch of images. Must be 3D or 4D.
        transform: Projective transform matrix/matrices. A vector of length 8 or
            tensor of size N x 8. If one row of transform is
            `[a0, a1, a2, b0, b1, b2, c0, c1]`, then it maps the output point
            `(x, y)` to a transformed input point
            `(x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k)`,
            where `k = c0 x + c1 y + 1`. The transform is inverted compared to
            the transform mapping input points to output points. Note that
            gradients are not backpropagated into transformation parameters.
            Note that `c0` and `c1` are only effective when using TensorFlow
            backend and will be considered as `0` when using other backends.
        interpolation: Interpolation method. Available methods are `"nearest"`,
            and `"bilinear"`. Defaults to `"bilinear"`.
        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.
        fill_value: Value used for points outside the boundaries of the input if
            `fill_mode="constant"`. Defaults to `0`.
        data_format: A string specifying the data format of the input tensor.
            It can be either `"channels_last"` or `"channels_first"`.
            `"channels_last"` corresponds to inputs with shape
            `(batch, height, width, channels)`, while `"channels_first"`
            corresponds to inputs with shape `(batch, channels, height, width)`.
            If not specified, the value will default to
            `keras.config.image_data_format`.

    Returns:
        Applied affine transform image or batch of images.

    Examples:

    >>> x = np.random.random((2, 64, 80, 3)) # batch of 2 RGB images
    >>> transform = np.array(
    ...     [
    ...         [1.5, 0, -20, 0, 1.5, -16, 0, 0],  # zoom
    ...         [1, 0, -20, 0, 1, -16, 0, 0],  # translation
    ...     ]
    ... )
    >>> y = keras.ops.image.affine_transform(x, transform)
    >>> y.shape
    (2, 64, 80, 3)

    >>> x = np.random.random((64, 80, 3)) # single RGB image
    >>> transform = np.array([1.0, 0.5, -20, 0.5, 1.0, -16, 0, 0])  # shear
    >>> y = keras.ops.image.affine_transform(x, transform)
    >>> y.shape
    (64, 80, 3)

    >>> x = np.random.random((2, 3, 64, 80)) # batch of 2 RGB images
    >>> transform = np.array(
    ...     [
    ...         [1.5, 0, -20, 0, 1.5, -16, 0, 0],  # zoom
    ...         [1, 0, -20, 0, 1, -16, 0, 0],  # translation
    ...     ]
    ... )
    >>> y = keras.ops.image.affine_transform(x, transform,
    ...     data_format="channels_first")
    >>> y.shape
    (2, 3, 64, 80)
    
keras/src/ops/image.py