When following the tutorial, I encountered the following problem, how to deal with this parameter

[ZZZZZHHHHHSSSSS]

(deformable_gaussian_env) E:\DeepLearning\Deformable-3D-Gaussians-main>python train.py -s E:\DeepLearning\Deformable-3D-Gaussians-main\data\D-NeRF\hook -m output/exp-name --eval --is_blender
Optimizing output/exp-name
Output folder: output/exp-name [29/05 13:04:22]
Tensorboard not available: not logging progress [29/05 13:04:22]
Found transforms_train.json file, assuming Blender data set! [29/05 13:04:22]
Reading Training Transforms [29/05 13:04:22]
Reading Test Transforms [29/05 13:04:26]
Loading Training Cameras [29/05 13:04:27]
Loading Test Cameras [29/05 13:04:30]
Number of points at initialisation : 100000 [29/05 13:04:30]
Training progress: 0%| | 0/40000 [00:00<?, ?it/s]Traceback (most recent call last):
File "train.py", line 275, in
training(lp.extract(args), op.extract(args), pp.extract(args), args.test_iterations, args.save_iterations)
File "train.py", line 100, in training
render_pkg_re = render(viewpoint_cam, gaussians, pipe, background, d_xyz, d_rotation, d_scaling, dataset.is_6dof)
File "E:\DeepLearning\Deformable-3D-Gaussians-main\gaussian_renderer_init_.py", line 117, in render
cov3D_precomp=cov3D_precomp)
File "E:\Anaconda3\envs\deformable_gaussian_env\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
TypeError: forward() got an unexpected keyword argument 'means2D_densify'

[ingra14m]

Hi, you may need to update the latest diff-gaussian-rasterization.

You can reclone the repo and get the latest rasterization. git clone https://github.com/ingra14m/Deformable-3D-Gaussians --recursive

[ZZZZZHHHHHSSSSS]

Hi, you may need to update the latest diff-gaussian-rasterization.

You can reclone the repo the get the latest rasterization. git clone https://github.com/ingra14m/Deformable-3D-Gaussians --recursive

Thank U very much！I'm still a vegitable bird（Chinglish） in the use of github. Thanks for being willing to answer such a basic question.