class_docstr: The ModelNet10 and ModelNet40 CAD models datasets from the paper:
  
  > [3D ShapeNets: A Deep Representation for Volumetric Shapes](https://arxiv.org/abs/1406.5670)<br>
  > Zhirong Wu et al.
  
  Each graph represents a CAD model belonging to one of 10 (or 40) categories.
  
  The models are polygon meshes: the node attributes are the 3d coordinates
  of the vertices, and edges are computed from each face. Duplicate edges are
  ignored and the adjacency matrix is binary.
  
  The dataset are pre-split into training and test sets: the `test` flag
  controls which split is loaded.
  
  **Arguments**
  
  - `name`: name of the dataset to load ('10' or '40');
  - `test`: if True, load the test set instead of the training set.
  - `n_jobs`: number of CPU cores to use for reading the data (-1, to use all
  available cores)
code: def read(self):
      folders = glob(osp.join(self.path, "*", ""))
      dataset = "test" if self.test else "train"
      classes = [f.split(os.path.sep)[-2] for f in folders]
      n_out = len(classes)
  
      print("Loading data")
  
      def load(fname, class_i):
          graph = load_off(fname)
          graph.y = one_hot(class_i, n_out)
          return graph
  
      output = []
      for i, c in enumerate(tqdm(classes)):
          fnames = osp.join(self.path, c, dataset, "{}_*.off".format(c))
          fnames = glob(fnames)
          output_partial = Parallel(n_jobs=self.n_jobs)(
              delayed(load)(fname, i) for fname in fnames
          )
          output.extend(output_partial)
  
      return output
  
file_path: spektral/spektral/datasets/modelnet.py
imports: import os
  import os.path
  import shutil
  from glob import glob
  from joblib import Parallel, delayed
  from tqdm import tqdm
  from spektral.data import Dataset
  from spektral.datasets.utils import download_file
  from spektral.utils import load_off, one_hot
label_desc: Use image node_id 3 for calling the ModelNet obj's underlying member method code with example usage: obj.read() and returns: output, graph