code: def fetch_lfw_people(
      data_home=None,
      funneled=True,
      resize=0.5,
      min_faces_per_person=0,
      color=False,
      slice_=(slice(70, 195), slice(78, 172)),
      download_if_missing=True,
  ):
      """Loader for the Labeled Faces in the Wild (LFW) people dataset
  
      This dataset is a collection of JPEG pictures of famous people
      collected on the internet, all details are available on the
      official website:
  
          http://vis-www.cs.umass.edu/lfw/
  
      Each picture is centered on a single face. Each pixel of each channel
      (color in RGB) is encoded by a float in range 0.0 - 1.0.
  
      The task is called Face Recognition (or Identification): given the
      picture of a face, find the name of the person given a training set
      (gallery).
  
      The original images are 250 x 250 pixels, but the default slice and resize
      arguments reduce them to 62 x 74.
  
      Parameters
      ----------
      data_home : optional, default: None
          Specify another download and cache folder for the datasets. By default
          all scikit learn data is stored in '~/scikit_learn_data' subfolders.
  
      funneled : boolean, optional, default: True
          Download and use the funneled variant of the dataset.
  
      resize : float, optional, default 0.5
          Ratio used to resize the each face picture.
  
      min_faces_per_person : int, optional, default None
          The extracted dataset will only retain pictures of people that have at
          least `min_faces_per_person` different pictures.
  
      color : boolean, optional, default False
          Keep the 3 RGB channels instead of averaging them to a single
          gray level channel. If color is True the shape of the data has
          one more dimension than the shape with color = False.
  
      slice_ : optional
          Provide a custom 2D slice (height, width) to extract the
          'interesting' part of the jpeg files and avoid use statistical
          correlation from the background
  
      download_if_missing : optional, True by default
          If False, raise a IOError if the data is not locally available
          instead of trying to download the data from the source site.
  
      Returns
      -------
      dataset : dict-like object with the following attributes:
  
      dataset.data : numpy array of shape (13233, 2914)
          Each row corresponds to a ravelled face image of original size 62 x 47
          pixels. Changing the ``slice_`` or resize parameters will change the
          shape of the output.
  
      dataset.images : numpy array of shape (13233, 62, 47)
          Each row is a face image corresponding to one of the 5749 people in
          the dataset. Changing the ``slice_`` or resize parameters will change
          the shape of the output.
  
      dataset.target : numpy array of shape (13233,)
          Labels associated to each face image. Those labels range from 0-5748
          and correspond to the person IDs.
  
      dataset.DESCR : string
          Description of the Labeled Faces in the Wild (LFW) dataset.
      """
      lfw_home, data_folder_path = check_fetch_lfw(
          data_home=data_home,
          funneled=funneled,
          download_if_missing=download_if_missing,
      )
      logger.info("Loading LFW people faces from %s", lfw_home)
  
      # wrap the loader in a memoizing function that will return memmaped data
      # arrays for optimal memory usage
      m = Memory(cachedir=lfw_home, compress=6, verbose=0)
      load_func = m.cache(_fetch_lfw_people)
  
      # load and memoize the pairs as np arrays
      faces, target, target_names = load_func(
          data_folder_path,
          resize=resize,
          min_faces_per_person=min_faces_per_person,
          color=color,
          slice_=slice_,
      )
  
      # pack the results as a Bunch instance
      return Bunch(
          data=faces.reshape(len(faces), -1),
          images=faces,
          target=target,
          target_names=target_names,
          DESCR="LFW faces dataset",
      )
  
file_path: catboost/contrib/python/scikit-learn/py2/sklearn/datasets/lfw.py
imports: from os import listdir, makedirs, remove, rename
  from os.path import join, exists, isdir
  from sklearn.utils import deprecated
  import logging
  import numpy
  from .base import get_data_home, Bunch
  from ..externals.joblib import Memory
  from ..externals.six import b
label_desc: Use image node_id 5 for calling a global function with example usage: fetch_lfw_people(data_home, funneled, resize, min_faces_per_person, color, slice_, download_if_missing) and returns: Bunch