def fixed_num_sampled_points_ambiguity(self):
        """
        return torch.Tensor([N,fixed_num,2]), in xmin, ymin, xmax, ymax form
            N means the num of instances
        """
        assert len(self.instance_list) != 0
        instance_points_list = []
        for instance in self.instance_list:
            distances = np.linspace(0, instance.length, self.fixed_num)
            sampled_points = np.array([list(instance.interpolate(distance).coords) for distance in distances]).reshape(-1, 2)
            instance_points_list.append(sampled_points)
        instance_points_array = np.array(instance_points_list)
        instance_points_tensor = to_tensor(instance_points_array)
        instance_points_tensor = instance_points_tensor.to(
                            dtype=torch.float32)
        instance_points_tensor[:,:,0] = torch.clamp(instance_points_tensor[:,:,0], min=-self.max_x,max=self.max_x)
        instance_points_tensor[:,:,1] = torch.clamp(instance_points_tensor[:,:,1], min=-self.max_y,max=self.max_y)
        instance_points_tensor = instance_points_tensor.unsqueeze(1)
        return instance_points_tensor

        return torch.Tensor([N,fixed_num,2]), in xmin, ymin, xmax, ymax form
            N means the num of instances
        
projects/mmdet3d_plugin/datasets/nuscenes_vad_dataset.py