Weight Tensor Dimension Issue when training on SemanticKitti

### Checklist

- [X] I have searched for [similar issues](https://github.com/isl-org/Open3D-ML/issues).
- [X] I have tested with the [latest development wheel](http://www.open3d.org/docs/latest/getting_started.html#development-version-pip).
- [X] I have checked the [release documentation](http://www.open3d.org/docs/release/) and the [latest documentation](http://www.open3d.org/docs/latest/) (for `master` branch).

### Describe the issue

I have been trying to train KPConv on Semantickitti using the Pytorch pipeline.

I use the default /home/user/Open3D-ML-master/ml3d/configs/kpconv_semantickitti.yml config file with adding "pin_memory: False" in pipeline.

The dataset was downloaded using the /home/user/Open3D-ML-master/scripts/download_datasets/download_semantickitti.sh script.

After successfully running through the preprocessing, I keep the getting the runtime error, that seemingly comes from an unexpected weight tensor dimension.

RuntimeError: weight tensor should be defined either for all 19 classes or no classes but got weight tensor of shape: [1, 19]

I get the same error using the RandLANet model.

Please give me any advice on how to deal with this issue.

### Steps to reproduce the bug

```python
import os
import open3d.ml as _ml3d
import open3d.ml.torch as ml3d

dataset = ml3d.datasets.SemanticKITTI(dataset_path='/Datasets/SemanticKitti', use_cache=True)

cfg_file = "/Open3D-ML-master/ml3d/configs/kpconv_semantickitti.yml"
cfg = _ml3d.utils.Config.load_from_file(cfg_file)

# create the model with random initialization.
model = ml3d.models.KPFCNN(**cfg.model)

pipeline = ml3d.pipelines.SemanticSegmentation(model=model, dataset=dataset,num_workers=1,device="cpu",**cfg.pipeline)

# prints training progress in the console.
pipeline.run_train()
```

### Error message

File "/home/user/anaconda3/envs/pointcloud_pytorch/lib/python3.10/site-packages/spyder_kernels/py3compat.py", line 356, in compat_exec
exec(code, globals, locals)

File "/home/user//Desktop/run_the_training.py", line 21, in
pipeline.run_train()

File "/home/user//anaconda3/envs/pointcloud_pytorch/lib/python3.10/site-packages/open3d/_ml3d/torch/pipelines/semantic_segmentation.py", line 411, in run_train
loss, gt_labels, predict_scores = model.get_loss(

File "/home/user//anaconda3/envs/pointcloud_pytorch/lib/python3.10/site-packages/open3d/_ml3d/torch/models/kpconv.py", line 339, in get_loss
self.output_loss = Loss.weighted_CrossEntropyLoss(scores, labels)

File "/home/user//anaconda3/envs/pointcloud_pytorch/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)

File "/home/user//anaconda3/envs/pointcloud_pytorch/lib/python3.10/site-packages/torch/nn/modules/loss.py", line 1164, in forward
return F.cross_entropy(input, target, weight=self.weight,

File "/home/user//anaconda3/envs/pointcloud_pytorch/lib/python3.10/site-packages/torch/nn/functional.py", line 3014, in cross_entropy
return torch._C._nn.cross_entropy_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index, label_smoothing)

RuntimeError: weight tensor should be defined either for all 19 classes or no classes but got weight tensor of shape: [1, 19]

### Expected behavior

I was expecting the model to train on the Semantic Kitti dataset. But, I keep getting the error.

### Open3D, Python and System information

```markdown
- Operating system: Ubuntu 20.04
- Python version: 3.10.6
- Open3D version: 0.16.0
- Is this remote workstation?: no
- How did you install Open3D?: pip
```

### Additional information

_No response_