class_docstr: Dataset to run forward inference on Shapenet kind of data data. Runs on a whole folder.
  Arguments:
      path: folder that contains a set of files of a given category
      category: index of the category to use for forward inference. This value depends on how many categories the model has been trained one.
      transforms: transforms to be applied to the data
      include_normals: wether to include normals for the forward inference
class_docstr_tok: ["Dataset","to","run","forward","inference","on","Shapenet","kind","of","data","data",".","Runs","on","a","whole","folder",".","Arguments",":","path",":","folder","that","contains","a","set","of","files","of","a","given","category","category",":","index","of","the","category","to","use","for","forward","inference",".","This","value","depends","on","how","many","categories","the","model","has","been","trained","one",".","transforms",":","transforms","to","be","applied","to","the","data","include_normals",":","wether","to","include","normals","for","the","forward","inference"]
code: def len(self):
      return len(self)
  
imports: import torch
  import glob
  import os
  from torch_geometric.io import read_txt_array
  from torch_geometric.data.data import Data
  import torch_geometric.transforms
  from torch_geometric.nn import knn_interpolate
  import numpy
  import logging
  from torch_points3d.core.data_transform import SaveOriginalPosId
  from torch_points3d.utils import is_list
  from torch_points3d.datasets.base_dataset import BaseDataset
  from torch_points3d.metrics.shapenet_part_tracker import ShapenetPartTracker
  from torch_points3d.datasets.segmentation.shapenet import ShapeNet
label_desc: Use image node_id 3 for calling the _ForwardShapenet obj's underlying member method code with example usage: obj.len() and returns: len