code: def __init__(self, modelnet, mode):
      super(ModelNetDataset, self).__init__()
      self.num_points = modelnet.num_points
      self.mode = mode
  
      if mode == "train":
          self.data = modelnet.f["train/data"][: modelnet.n_train]
          self.label = modelnet.f["train/label"][: modelnet.n_train]
      elif mode == "valid":
          self.data = modelnet.f["train/data"][modelnet.n_train :]
          self.label = modelnet.f["train/label"][modelnet.n_train :]
      elif mode == "test":
          self.data = modelnet.f["test/data"].value
          self.label = modelnet.f["test/label"].value
  
file_path: dgl/examples/pytorch/pointcloud/edgeconv/modelnet.py
imports: import numpy
  from torch.utils.data import Dataset
label_desc: Use image node_id 1 to create a new ModelNetDataset object from inherited base classes: Dataset with example: obj = ModelNetDataset(modelnet, mode)