def METHOD_NAME(self, input):
    num_dim = input.dim()
    assert num_dim in [3, 4], f'expected the input to have 3 or 4 dimensions, but got {num_dim} dimensions'
    h_in, w_in = input.shape[-2:]
    def _convert_int_to_list(item):
        if isinstance(item, int):
            return [item] * 2
        else:
            return item
    padding = _convert_int_to_list(self.padding)
    dilation = _convert_int_to_list(self.dilation)
    kernel_size = _convert_int_to_list(self.kernel_size)
    stride = _convert_int_to_list(self.stride)
    h_out = math.floor((h_in + 2 * padding[0] - dilation[0] * (kernel_size[0] - 1) - 1) / stride[0] + 1)
    w_out = math.floor((w_in + 2 * padding[1] - dilation[1] * (kernel_size[1] - 1) - 1) / stride[1] + 1)
    result_shape = tuple(input.shape[:-2]) + (
        h_out,
        w_out,
    )
    return torch.empty(result_shape, device='meta')