class_docstr: CelebA dataset. Aligned and cropped. With metadata.
class_docstr_tok: ["CelebA","dataset",".","Aligned","and","cropped",".","With","metadata","."]
docstr: Unpack the celeba config file.
  
  The file starts with the number of lines, and a header.
  Afterwards, there is a configuration for each file: one per line.
  
  Args:
    file_path: Path to the file with the configuration.
    keys_list: List of names of the attributes.
  
  Returns:
    keys: names of the attributes
    values: map from the file name to the list of attribute values for
            this file.
docstr_tok: ["Unpack","the","celeba","config","file",".","The","file","starts","with","the","number","of","lines",",","and","a","header",".","Afterwards",",","there","is","a","configuration","for","each","file",":","one","per","line",".","Args",":","file_path",":","Path","to","the","file","with","the","configuration",".","keys_list",":","List","of","names","of","the","attributes",".","Returns",":","keys",":","names","of","the","attributes","values",":","map","from","the","file","name","to","the","list","of","attribute","values","for","this","file","."]
code: def _process_celeba_nonheading_config_file(
      self,
      file_path: Union[os.PathLike[str], str],
      keys_list: Sequence[str],
  ):
      """Unpack the celeba config file.
  
      The file starts with the number of lines, and a header.
      Afterwards, there is a configuration for each file: one per line.
  
      Args:
        file_path: Path to the file with the configuration.
        keys_list: List of names of the attributes.
  
      Returns:
        keys: names of the attributes
        values: map from the file name to the list of attribute values for
                this file.
      """
      with epath.Path(file_path).open() as f:
          data_raw = f.read()
      lines = data_raw.split("\n")
  
      keys = keys_list
      values = {}
      # Go over each line (skip the last one, as it is empty).
      for line in lines[0:-1]:
          row_values = line.strip().split()
          # Each row start with the 'file_name' and then space-separated values.
          values[row_values[0]] = [int(v) for v in row_values[1:]]
      return keys, values
  
file_path: datasets/tensorflow_datasets/datasets/celeb_a/celeb_a_dataset_builder.py
imports: from __future__ import annotations
  import os
  from typing import Sequence, Union
  from etils import epath
  import numpy
  import tensorflow_datasets.public_api
label_desc: Use image node_id 4 for calling the Builder obj's underlying member method code with example usage: obj._process_celeba_nonheading_config_file(file_path, keys_list) and returns: keys,  values