code: def __init__(
      self,
      feature_size=0,
      transform=None,
      num_points=100,
      panoptic=False,
      include_box=False,
      batch_size=2,
  ):
      self.feature_dimension = feature_size
      self.num_classes = 10
      self.num_points = num_points
      self.batch_size = batch_size
      self.weight_classes = None
      self.feature_size = feature_size
      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.randint(0, 10, (self.num_points,))
      self._category = torch.ones((self.num_points,), dtype=torch.long)
      self._ms_transform = None
      self._transform = transform
      self.mean_size_arr = torch.rand((11, 3))
      self.include_box = include_box
      self.panoptic = panoptic
  
imports: import numpy
  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
  from torch_points3d.datasets.registration.pair import Pair, PairBatch, PairMultiScaleBatch, DensePairBatch
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, panoptic, include_box, batch_size)