Summarize the question. AttributeError: 'ConfigDict' object has no attribute 'ckpt_path'

### 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 `main` branch).

### My Question

I am getting an error of "
AttributeError: 'ConfigDict' object has no attribute 'ckpt_path'" for the last line of the code. Here is my code: import open3d.ml as _ml3d
import open3d.ml.torch as ml3d # or open3d.ml.tf as ml3d

framework = "torch" # or tf
cfg_file = "/content/Open3D-ML/ml3d/configs/pvcnn_s3dis.yml"
cfg = _ml3d.utils.Config.load_from_file(cfg_file)

# fetch the classes by the name
Pipeline = _ml3d.utils.get_module("pipeline", cfg.pipeline.name, framework)
Model = _ml3d.utils.get_module("model", cfg.model.name, framework)
Dataset = _ml3d.utils.get_module("dataset", cfg.dataset.name)

# use the arguments in the config file to construct the instances
cfg.dataset['dataset_path'] = "/content/drive/MyDrive/S3DIS/Stanford3dDataset_v1.2_Aligned_Version-sample"
dataset = Dataset(cfg.dataset.pop('dataset_path', None), **cfg.dataset)
model = Model(**cfg.model)
pipeline = Pipeline(model, dataset, **cfg.pipeline)
import open3d.ml.torch as ml3d
from open3d.ml.torch.models import PVCNN
from open3d.ml.torch.pipelines import SemanticSegmentation

# use a cache for storing the results of the preprocessing (default path is './logs/cache')
dataset = ml3d.datasets.S3DIS(dataset_path='/content/drive/MyDrive/S3DIS/Stanford3dDataset_v1.2_Aligned_Version-sample',ckpt_path="/content/drive/MyDrive/S3DIS/checkpoint", use_cache=True )

# create the model with random initialization.
model = PVCNN()

pipeline = SemanticSegmentation(model=model, dataset=dataset, **cfg.pipeline)

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