code: def load_nci(featurizer="ECFP", shard_size=1000, split="random"):
      current_dir = os.path.dirname(os.path.realpath(__file__))
  
      # Load nci dataset
      print("About to load NCI dataset.")
      dataset_path = os.path.join(
          current_dir, "../../datasets/nci_unique.csv"
      )
  
      # Featurize nci dataset
      print("About to featurize nci dataset.")
      if featurizer == "ECFP":
          featurizer = dc.feat.CircularFingerprint(size=1024)
      elif featurizer == "GraphConv":
          featurizer = dc.feat.ConvMolFeaturizer()
  
      all_nci_tasks = [
          "CCRF-CEM",
          "HL-60(TB)",
          "K-562",
          "MOLT-4",
          "RPMI-8226",
          "SR",
          "A549/ATCC",
          "EKVX",
          "HOP-62",
          "HOP-92",
          "NCI-H226",
          "NCI-H23",
          "NCI-H322M",
          "NCI-H460",
          "NCI-H522",
          "COLO 205",
          "HCC-2998",
          "HCT-116",
          "HCT-15",
          "HT29",
          "KM12",
          "SW-620",
          "SF-268",
          "SF-295",
          "SF-539",
          "SNB-19",
          "SNB-75",
          "U251",
          "LOX IMVI",
          "MALME-3M",
          "M14",
          "MDA-MB-435",
          "SK-MEL-2",
          "SK-MEL-28",
          "SK-MEL-5",
          "UACC-257",
          "UACC-62",
          "IGR-OV1",
          "OVCAR-3",
          "OVCAR-4",
          "OVCAR-5",
          "OVCAR-8",
          "NCI/ADR-RES",
          "SK-OV-3",
          "786-0",
          "A498",
          "ACHN",
          "CAKI-1",
          "RXF 393",
          "SN12C",
          "TK-10",
          "UO-31",
          "PC-3",
          "DU-145",
          "MCF7",
          "MDA-MB-231/ATCC",
          "MDA-MB-468",
          "HS 578T",
          "BT-549",
          "T-47D",
      ]
  
      loader = dc.data.CSVLoader(
          tasks=all_nci_tasks,
          smiles_field="smiles",
          featurizer=featurizer,
      )
  
      dataset = loader.featurize(dataset_path, shard_size=shard_size)
  
      # Initialize transformers
      print("About to transform data")
      transformers = [
          dc.trans.NormalizationTransformer(
              transform_y=True, dataset=dataset
          )
      ]
      for transformer in transformers:
          dataset = transformer.transform(dataset)
  
      splitters = {
          "index": dc.splits.IndexSplitter(),
          "random": dc.splits.RandomSplitter(),
          "scaffold": dc.splits.ScaffoldSplitter(),
      }
      splitter = splitters[split]
      print("Performing new split.")
      train, valid, test = splitter.train_valid_test_split(dataset)
  
      return all_nci_tasks, (train, valid, test), transformers
  
file_path: deepchem/examples/nci/nci_datasets.py
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import unicode_literals
  import os
  import sys
  import numpy
  import shutil
  import deepchem
label_desc: Use image node_id 1 for calling a global function with example usage: load_nci(featurizer, shard_size, split) and returns: all_nci_tasks,  train,  valid,  test,  transformers