code: def molecule_datapipe() -> IterDataPipe:
      # Download HIV dataset from MoleculeNet:
      url = "https://deepchemdata.s3-us-west-1.amazonaws.com/datasets"
      root_dir = osp.join(
          osp.dirname(osp.realpath(__file__)), "..", "data"
      )
      path = download_url(f"{url}/HIV.csv", root_dir)
  
      datapipe = FileOpener([path])
      datapipe = datapipe.parse_csv_as_dict()
      datapipe = datapipe.parse_smiles(target_key="HIV_active")
      datapipe = (
          datapipe.in_memory_cache()
      )  # Cache graph instances in-memory.
  
      return datapipe
  
file_path: pytorch_geometric/examples/datapipe.py
imports: import argparse
  import os.path
  import time
  import torch
  from torchdata.datapipes.iter import FileLister, FileOpener, IterDataPipe
  from torch_geometric.data import Data, download_url, extract_zip
label_desc: Use image node_id 1 for calling a global function with example usage: molecule_datapipe() and returns: datapipe