keras/src/backend/tensorflow/image.py/affine_transform
def affine_transform(
    images,
    transform,
    interpolation="bilinear",
    fill_mode="constant",
    fill_value=0,
    data_format=None,
):
    data_format = backend.standardize_data_format(data_format)
    if interpolation not in AFFINE_TRANSFORM_INTERPOLATIONS:
        raise ValueError(
            "Invalid value for argument `interpolation`. Expected of one "
            f"{AFFINE_TRANSFORM_INTERPOLATIONS}. Received: "
            f"interpolation={interpolation}"
        )
    if fill_mode not in AFFINE_TRANSFORM_FILL_MODES:
        raise ValueError(
            "Invalid value for argument `fill_mode`. Expected of one "
            f"{AFFINE_TRANSFORM_FILL_MODES}. Received: fill_mode={fill_mode}"
        )
    if len(images.shape) not in (3, 4):
        raise ValueError(
            "Invalid images rank: expected rank 3 (single image) "
            "or rank 4 (batch of images). Received input with shape: "
            f"images.shape={images.shape}"
        )
    if len(transform.shape) not in (1, 2):
        raise ValueError(
            "Invalid transform rank: expected rank 1 (single transform) "
            "or rank 2 (batch of transforms). Received input with shape: "
            f"transform.shape={transform.shape}"
        )
    # unbatched case
    need_squeeze = False
    if len(images.shape) == 3:
        images = tf.expand_dims(images, axis=0)
        need_squeeze = True
    if len(transform.shape) == 1:
        transform = tf.expand_dims(transform, axis=0)

    if data_format == "channels_first":
        images = tf.transpose(images, (0, 2, 3, 1))

    affined = tf.raw_ops.ImageProjectiveTransformV3(
        images=images,
        transforms=tf.cast(transform, dtype=tf.float32),
        output_shape=tf.shape(images)[1:-1],
        fill_value=fill_value,
        interpolation=interpolation.upper(),
        fill_mode=fill_mode.upper(),
    )
    affined = tf.ensure_shape(affined, images.shape)

    if data_format == "channels_first":
        affined = tf.transpose(affined, (0, 3, 1, 2))
    if need_squeeze:
        affined = tf.squeeze(affined, axis=0)
    return affined