def METHOD_NAME(
    input: torch.Tensor,
    size: Optional[Union[int, Tuple[int], Tuple[int, int], Tuple[int, int,
                                                                 int]]] = None,
    scale_factor: Optional[Union[float, Tuple[float]]] = None,
    mode: str = 'nearest',
    align_corners: Optional[bool] = None,
    recompute_scale_factor: Optional[bool] = None,
):
    """Register default symbolic function for `interpolate`."""
    ctx = FUNCTION_REWRITER.get_context()
    class BicubicInterpolate(Function):
        def __init__(self) -> None:
            super().__init__()
        @staticmethod
        def symbolic(g, input, scale_factor, align_corners):
            """Symbolic function for creating onnx op."""
            return g.op(
                'mmdeploy::TRTBicubicInterpolate',
                input,
                scale_factor_f=scale_factor,
                align_corners_i=align_corners)
        @staticmethod
        def forward(g, input, scale_factor, align_corners):
            """Run forward."""
            return ctx.origin_func(
                input,
                scale_factor=scale_factor,
                mode='bicubic',
                align_corners=align_corners)
    if 'bicubic' == mode:
        input_size = input.shape
        if isinstance(scale_factor, float):
            scale_factor = [scale_factor, scale_factor]
        if scale_factor is None:
            logger = get_root_logger()
            logger.warning(
                'ResizeLayer in TensorRT allow dynamic input shape with shape '
                'tensor. Which is not available for custom ops. Computed scale'
                '_factor might be the right way to get final shape.')
            scale_factor = [
                float(s_out / s_in)
                for s_out, s_in in zip(size, input_size[2:])
            ]
        return BicubicInterpolate.apply(input, scale_factor, align_corners)
    else:
        return ctx.origin_func(
            input,
            size=size,
            scale_factor=scale_factor,
            mode=mode,
            align_corners=align_corners,
            recompute_scale_factor=recompute_scale_factor)