code: def load_criteo(dataset_folder="dataset/"):
      # Data processing code adapted from https://github.com/facebookresearch/dlrm
      # Follow steps in https://github.com/ylongqi/dlrm/blob/master/data_utils.py to generate kaggle_processed.npz
      # Or using `./download_dataset.sh criteo` command to download the processed data.
  
      with np.load(
          dataset_folder + "criteo/kaggle_processed.npz"
      ) as data:
          X_int = data["X_int"]
          X_cat = data["X_cat"]
          y = data["y"]
          counts = data["counts"]
  
      indices = np.arange(len(y))
      indices = np.array_split(indices, 7)
      for i in range(len(indices)):
          indices[i] = np.random.permutation(indices[i])
  
      train_indices = np.concatenate(indices[:-1])
      test_indices = indices[-1]
      val_indices, test_indices = np.array_split(test_indices, 2)
      train_indices = np.random.permutation(train_indices)
  
      raw_data = dict()
  
      raw_data["counts"] = counts
  
      raw_data["X_cat_train"] = X_cat[train_indices].astype(np.int32)
      raw_data["X_int_train"] = np.log(X_int[train_indices] + 1).astype(
          np.float32
      )
      raw_data["y_train"] = y[train_indices].astype(np.float32)
  
      raw_data["X_cat_val"] = X_cat[val_indices]
      raw_data["X_int_val"] = np.log(X_int[val_indices] + 1).astype(
          np.float32
      )
      raw_data["y_val"] = y[val_indices]
  
      raw_data["X_cat_test"] = X_cat[test_indices]
      raw_data["X_int_test"] = np.log(X_int[test_indices] + 1).astype(
          np.float32
      )
      raw_data["y_test"] = y[test_indices]
  
      return raw_data
  
imports: import tensorflow
  import numpy
label_desc: Use image node_id 4 for calling a global function with example usage: load_criteo(dataset_folder) and returns: raw_data