code: def process(self):
      print("Beginning Processing ...")
  
      # Load the file with the list of functions.
      classes_ = {}
      with open(self.root + "/class_map.txt", "r") as mFile:
          for line in mFile:
              lineList = line.rstrip().split("\t")
              classes_[lineList[0]] = int(lineList[1])
  
      # Get the file list.
      fileList_ = []
      cathegories_ = []
      with open(self.root + "/" + self.split + ".txt", "r") as mFile:
          for curLine in mFile:
              splitLine = curLine.rstrip().split("\t")
              curClass = classes_[splitLine[-1]]
              fileList_.append(
                  self.root + "/" + self.split + "/" + splitLine[0]
              )
              cathegories_.append(curClass)
  
      # Load the dataset
      print("Reading the data")
      with warnings.catch_warnings():
          warnings.simplefilter("ignore")
          data_list = []
          for fileIter, curFile in tqdm(enumerate(fileList_)):
              fileName = curFile.split("/")[-1]
              curProtein = self.protein_to_graph(curFile + ".hdf5")
              curProtein.id = fileName
              curProtein.y = torch.tensor(cathegories_[fileIter])
              if not curProtein.x is None:
                  data_list.append(curProtein)
      data, slices = self.collate(data_list)
      torch.save((data, slices), self.processed_paths[0])
      print("Done!")
  
imports: import os.path
  import h5py
  import numpy
  import warnings
  from tqdm import tqdm
  import torch
  import torch.nn.functional
  from torch_geometric.data import Data
  from torch_geometric.data import InMemoryDataset
label_desc: Use image node_id 11 for calling the FOLDdataset obj's underlying member method code with example usage: obj.process() without return types