class_docstr: The ModelNet10/40 dataset from the `"3D ShapeNets: A Deep
  Representation for Volumetric Shapes"
  <https://people.csail.mit.edu/khosla/papers/cvpr2015_wu.pdf>`_ paper,
  containing sampled CAD models of 40 categories. Each sample contains 10,000
  points uniformly sampled with their normal vector.
  
  .. note::
  
      Data objects hold mesh faces instead of edge indices.
      To convert the mesh to a graph, use the
      :obj:`torch_geometric.transforms.FaceToEdge` as :obj:`pre_transform`.
      To convert the mesh to a point cloud, use the
      :obj:`torch_geometric.transforms.SamplePoints` as :obj:`transform` to
      sample a fixed number of points on the mesh faces according to their
      face area.
  
  Parameters:
  ------------
  root (string): Root directory where the dataset should be saved.
  name (string, optional): The name of the dataset (:obj:`"10"` for
      ModelNet10, :obj:`"40"` for ModelNet40). (default: :obj:`"10"`)
  train (bool, optional): If :obj:`True`, loads the training dataset,
      otherwise the test dataset. (default: :obj:`True`)
  transform (callable, optional): A function/transform that takes in an
      :obj:`torch_geometric.data.Data` object and returns a transformed
      version. The data object will be transformed before every access.
      (default: :obj:`None`)
  pre_transform (callable, optional): A function/transform that takes in
      an :obj:`torch_geometric.data.Data` object and returns a
      transformed version. The data object will be transformed before
      being saved to disk. (default: :obj:`None`)
  pre_filter (callable, optional): A function that takes in an
      :obj:`torch_geometric.data.Data` object and returns a boolean
      value, indicating whether the data object should be included in the
      final dataset. (default: :obj:`None`)
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code: def __repr__(self):
      return "{}{}({})".format(
          self.__class__.__name__, self.name, len(self)
      )
  
imports: import os.path
  import os
  import shutil
  import torch
  from torch_geometric.datasets import ModelNet
  from torch_geometric.data import DataLoader, InMemoryDataset, extract_zip, Data
  import torch_geometric.transforms
  from torch_geometric.io import read_txt_array
  from torch_points3d.datasets.base_dataset import BaseDataset
  from torch_points3d.metrics.classification_tracker import ClassificationTracker
  from torch_points3d.utils.download import download_url
label_desc: Use image node_id 7 for calling the SampledModelNet obj's underlying member method code with example usage: obj.__repr__() and returns: str