code: def __init__(self, feature_size=0, transform=None, num_points=100):
      self.feature_dimension = feature_size
      self.num_classes = 10
      self.num_points = num_points
      self.batch_size = 2
      self.weight_classes = None
      if feature_size > 0:
          self._feature = torch.tensor(
              [range(feature_size) for i in range(self.num_points)],
              dtype=torch.float,
          )
      else:
          self._feature = None
      self._y = torch.tensor(
          [0 for i in range(self.num_points)], dtype=torch.long
      )
      self._category = torch.ones((self.num_points,), dtype=torch.long)
      self._ms_transform = None
      self._transform = transform
  
imports: import torch
  from torch_geometric.data import Data, Batch
  from torch_points3d.datasets.batch import SimpleBatch
  from torch_points3d.core.data_transform import MultiScaleTransform
  from torch_points3d.datasets.multiscale_data import MultiScaleBatch
label_desc: Use image node_id 1 to create a new MockDataset object from inherited base classes: torch.utils.data with example: obj = MockDataset(feature_size, transform, num_points)