class_docstr: The FAUST humans dataset from the `"FAUST: Dataset and Evaluation for
  3D Mesh Registration"
  <http://files.is.tue.mpg.de/black/papers/FAUST2014.pdf>`_ paper,
  containing 100 watertight meshes representing 10 different poses for 10
  different subjects.
  
  .. 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.
  
  Args:
      root (str): Root directory where the dataset should be saved.
      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`)
      force_reload (bool, optional): Whether to re-process the dataset.
          (default: :obj:`False`)
  
  **STATS:**
  
  .. list-table::
      :widths: 10 10 10 10 10
      :header-rows: 1
  
      * - #graphs
        - #nodes
        - #edges
        - #features
        - #classes
      * - 100
        - 6,890
        - 41,328
        - 3
        - 10
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code: def __init__(
      self,
      root: str,
      train: bool = True,
      transform: Optional[Callable] = None,
      pre_transform: Optional[Callable] = None,
      pre_filter: Optional[Callable] = None,
      force_reload: bool = False,
  ) -> None:
      super().__init__(
          root,
          transform,
          pre_transform,
          pre_filter,
          force_reload=force_reload,
      )
      path = (
          self.processed_paths[0] if train else self.processed_paths[1]
      )
      self.load(path)
  
file_path: pytorch_geometric/torch_geometric/datasets/faust.py
imports: import os.path
  from typing import Callable, List, Optional
  import torch
  from torch_geometric.data import InMemoryDataset, extract_zip
  from torch_geometric.io import fs, read_ply
label_desc: Use image node_id 1 to create a new FAUST object from inherited base classes: InMemoryDataset with example: obj = FAUST(root, train, transform, pre_transform, pre_filter, force_reload)