code: def read_energy(file: str, chunk_size: int) -> np.ndarray:
      import pandas as pd
  
      def skipatoms(i: int) -> bool:
          return (i - 1) % chunk_size != 0
  
      if chunk_size - 2 == 11 * 3:
          # Manually handle bad lines in W11
          df = pd.read_table(
              file, header=None, dtype="string", skiprows=skipatoms
          )
          df = df[0].str.split().str[-1].astype(np.float32)
      else:
          df = pd.read_table(
              file,
              sep=r"\s+",
              names=["label", "energy"],
              dtype=np.float32,
              skiprows=skipatoms,
              usecols=["energy"],
              memory_map=True,
          )
  
      return df.to_numpy().squeeze()
  
imports: import copy
  import os
  import os.path
  from dataclasses import dataclass
  from functools import cached_property
  from glob import glob
  from pathlib import Path
  from typing import Callable, List, MutableSequence, Optional, Tuple, Union
  import numpy
  import torch
  from torch.utils.data import ConcatDataset, Subset
  from torch_geometric.data import Data, InMemoryDataset, download_url, extract_zip
  from torch_geometric.data.data import BaseData
label_desc: Use image node_id 2 for calling a global function with example usage: read_energy(file, chunk_size) and returns: df, unknown