code: def process(self):
      print("Beginning Processing ...")
  
      # Load the file with the list of functions.
      functions_ = []
      with open(self.root + "/unique_functions.txt", "r") as mFile:
          for line in mFile:
              functions_.append(line.rstrip())
  
      # Get the file list.
      if self.split == "Train":
          splitFile = "/training.txt"
      elif self.split == "Val":
          splitFile = "/validation.txt"
      elif self.split == "Test":
          splitFile = "/testing.txt"
  
      proteinNames_ = []
      fileList_ = []
      with open(self.root + splitFile, "r") as mFile:
          for line in mFile:
              proteinNames_.append(line.rstrip())
              fileList_.append(self.root + "/data/" + line.rstrip())
  
      # Load the functions.
      print("Reading protein functions")
      protFunct_ = {}
      with open(self.root + "/chain_functions.txt", "r") as mFile:
          for line in mFile:
              splitLine = line.rstrip().split(",")
              if splitLine[0] in proteinNames_:
                  protFunct_[splitLine[0]] = int(splitLine[1])
  
      # 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(
                  protFunct_[proteinNames_[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 ECdataset obj's underlying member method code with example usage: obj.process() without return types