def METHOD_NAME(
    self,
    input: Tensor,
    params: dict[str, Tensor],
    flags: dict[str, Any],
    transform: Optional[Tensor] = None,
) -> Tensor:
    """Apply the transform.
    Args:
        input: the input tensor
        params: generated parameters
        flags: static parameters
        transform: the geometric transformation tensor
    Returns:
        the augmented input
    """
    out = []
    for i in range(flags["num"]):
        out.append(crop_by_indices(input, params["src"][i], flags["size"]))
    return torch.cat(out)