code: def __init__(self, feature_size=6):
      self.feature_dimension = feature_size
      self.num_classes = 10
      self.weight_classes = None
      nb_points = 100
      self._pos = torch.randn((nb_points, 3))
      if feature_size > 0:
          self._feature = torch.tensor(
              [range(feature_size) for i in range(self._pos.shape[0])],
              dtype=torch.float,
          )
      else:
          self._feature = None
      self._y = torch.tensor(
          [range(10) for i in range(self._pos.shape[0])],
          dtype=torch.float,
      )
      self._batch = torch.tensor([0 for i in range(self._pos.shape[0])])
  
file_path: torch-points3d/test/test_resolver.py
imports: import unittest
  from omegaconf import OmegaConf
  import torch
  from torch.utils.data import Dataset
  from torch_geometric.data import Batch
  import os
  import sys
  from torch_points3d.utils.model_building_utils.model_definition_resolver import resolve_model
label_desc: Use image node_id 1 to create a new MockDataset object from inherited base classes: Dataset with example: obj = MockDataset(feature_size)