code: def fetch_20newsgroups(
      data_home=None,
      subset="train",
      categories=None,
      shuffle=True,
      random_state=42,
      remove=(),
      download_if_missing=True,
  ):
      """Load the filenames and data from the 20 newsgroups dataset.
  
      Read more in the :ref:`User Guide <20newsgroups>`.
  
      Parameters
      ----------
      subset : 'train' or 'test', 'all', optional
          Select the dataset to load: 'train' for the training set, 'test'
          for the test set, 'all' for both, with shuffled ordering.
  
      data_home : optional, default: None
          Specify a download and cache folder for the datasets. If None,
          all scikit-learn data is stored in '~/scikit_learn_data' subfolders.
  
      categories : None or collection of string or unicode
          If None (default), load all the categories.
          If not None, list of category names to load (other categories
          ignored).
  
      shuffle : bool, optional
          Whether or not to shuffle the data: might be important for models that
          make the assumption that the samples are independent and identically
          distributed (i.i.d.), such as stochastic gradient descent.
  
      random_state : numpy random number generator or seed integer
          Used to shuffle the dataset.
  
      download_if_missing : optional, True by default
          If False, raise an IOError if the data is not locally available
          instead of trying to download the data from the source site.
  
      remove : tuple
          May contain any subset of ('headers', 'footers', 'quotes'). Each of
          these are kinds of text that will be detected and removed from the
          newsgroup posts, preventing classifiers from overfitting on
          metadata.
  
          'headers' removes newsgroup headers, 'footers' removes blocks at the
          ends of posts that look like signatures, and 'quotes' removes lines
          that appear to be quoting another post.
  
          'headers' follows an exact standard; the other filters are not always
          correct.
      """
  
      data_home = get_data_home(data_home=data_home)
      cache_path = _pkl_filepath(data_home, CACHE_NAME)
      twenty_home = os.path.join(data_home, "20news_home")
      cache = None
      if os.path.exists(cache_path):
          try:
              with open(cache_path, "rb") as f:
                  compressed_content = f.read()
              uncompressed_content = codecs.decode(
                  compressed_content, "zlib_codec"
              )
              cache = pickle.loads(uncompressed_content)
          except Exception as e:
              print(80 * "_")
              print("Cache loading failed")
              print(80 * "_")
              print(e)
  
      if cache is None:
          if download_if_missing:
              logger.info(
                  "Downloading 20news dataset. "
                  "This may take a few minutes."
              )
              cache = download_20newsgroups(
                  target_dir=twenty_home, cache_path=cache_path
              )
          else:
              raise IOError("20Newsgroups dataset not found")
  
      if subset in ("train", "test"):
          data = cache[subset]
      elif subset == "all":
          data_lst = list()
          target = list()
          filenames = list()
          for subset in ("train", "test"):
              data = cache[subset]
              data_lst.extend(data.data)
              target.extend(data.target)
              filenames.extend(data.filenames)
  
          data.data = data_lst
          data.target = np.array(target)
          data.filenames = np.array(filenames)
      else:
          raise ValueError(
              "subset can only be 'train', 'test' or 'all', got '%s'"
              % subset
          )
  
      data.description = "the 20 newsgroups by date dataset"
  
      if "headers" in remove:
          data.data = [
              strip_newsgroup_header(text) for text in data.data
          ]
      if "footers" in remove:
          data.data = [
              strip_newsgroup_footer(text) for text in data.data
          ]
      if "quotes" in remove:
          data.data = [
              strip_newsgroup_quoting(text) for text in data.data
          ]
  
      if categories is not None:
          labels = [
              (data.target_names.index(cat), cat) for cat in categories
          ]
          # Sort the categories to have the ordering of the labels
          labels.sort()
          labels, categories = zip(*labels)
          mask = np.in1d(data.target, labels)
          data.filenames = data.filenames[mask]
          data.target = data.target[mask]
          # searchsorted to have continuous labels
          data.target = np.searchsorted(labels, data.target)
          data.target_names = list(categories)
          # Use an object array to shuffle: avoids memory copy
          data_lst = np.array(data.data, dtype=object)
          data_lst = data_lst[mask]
          data.data = data_lst.tolist()
  
      if shuffle:
          random_state = check_random_state(random_state)
          indices = np.arange(data.target.shape[0])
          random_state.shuffle(indices)
          data.filenames = data.filenames[indices]
          data.target = data.target[indices]
          # Use an object array to shuffle: avoids memory copy
          data_lst = np.array(data.data, dtype=object)
          data_lst = data_lst[indices]
          data.data = data_lst.tolist()
  
      return data
  
imports: import os
  import logging
  import tarfile
  import pickle
  import shutil
  import re
  import codecs
  import numpy
  import scipy.sparse
  from .base import get_data_home
  from .base import Bunch
  from .base import load_files
  from .base import _pkl_filepath
  from ..utils import check_random_state
  from ..feature_extraction.text import CountVectorizer
  from ..preprocessing import normalize
  from ..externals import joblib, six
label_desc: Use image node_id 5 for calling a global function with example usage: fetch_20newsgroups(data_home, subset, categories, shuffle, random_state, remove, download_if_missing) and returns: data