code: def load_files(
      container_path,
      description=None,
      categories=None,
      load_content=True,
      shuffle=True,
      encoding=None,
      decode_error="strict",
      random_state=0,
  ):
      """Load text files with categories as subfolder names.
  
      Individual samples are assumed to be files stored a two levels folder
      structure such as the following:
  
          container_folder/
              category_1_folder/
                  file_1.txt
                  file_2.txt
                  ...
                  file_42.txt
              category_2_folder/
                  file_43.txt
                  file_44.txt
                  ...
  
      The folder names are used as supervised signal label names. The
      individual file names are not important.
  
      This function does not try to extract features into a numpy array or
      scipy sparse matrix. In addition, if load_content is false it
      does not try to load the files in memory.
  
      To use text files in a scikit-learn classification or clustering
      algorithm, you will need to use the `sklearn.feature_extraction.text`
      module to build a feature extraction transformer that suits your
      problem.
  
      If you set load_content=True, you should also specify the encoding of
      the text using the 'encoding' parameter. For many modern text files,
      'utf-8' will be the correct encoding. If you leave encoding equal to None,
      then the content will be made of bytes instead of Unicode, and you will
      not be able to use most functions in `sklearn.feature_extraction.text`.
  
      Similar feature extractors should be built for other kind of unstructured
      data input such as images, audio, video, ...
  
      Read more in the :ref:`User Guide <datasets>`.
  
      Parameters
      ----------
      container_path : string or unicode
          Path to the main folder holding one subfolder per category
  
      description: string or unicode, optional (default=None)
          A paragraph describing the characteristic of the dataset: its source,
          reference, etc.
  
      categories : A collection of strings or None, optional (default=None)
          If None (default), load all the categories.
          If not None, list of category names to load (other categories ignored).
  
      load_content : boolean, optional (default=True)
          Whether to load or not the content of the different files. If
          true a 'data' attribute containing the text information is present
          in the data structure returned. If not, a filenames attribute
          gives the path to the files.
  
      encoding : string or None (default is None)
          If None, do not try to decode the content of the files (e.g. for
          images or other non-text content).
          If not None, encoding to use to decode text files to Unicode if
          load_content is True.
  
      decode_error: {'strict', 'ignore', 'replace'}, optional
          Instruction on what to do if a byte sequence is given to analyze that
          contains characters not of the given `encoding`. Passed as keyword
          argument 'errors' to bytes.decode.
  
      shuffle : bool, optional (default=True)
          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 : int, RandomState instance or None, optional (default=0)
          If int, random_state is the seed used by the random number generator;
          If RandomState instance, random_state is the random number generator;
          If None, the random number generator is the RandomState instance used
          by `np.random`.
  
      Returns
      -------
      data : Bunch
          Dictionary-like object, the interesting attributes are: either
          data, the raw text data to learn, or 'filenames', the files
          holding it, 'target', the classification labels (integer index),
          'target_names', the meaning of the labels, and 'DESCR', the full
          description of the dataset.
      """
      target = []
      target_names = []
      filenames = []
  
      folders = [
          f
          for f in sorted(listdir(container_path))
          if isdir(join(container_path, f))
      ]
  
      if categories is not None:
          folders = [f for f in folders if f in categories]
  
      for label, folder in enumerate(folders):
          target_names.append(folder)
          folder_path = join(container_path, folder)
          documents = [
              join(folder_path, d) for d in sorted(listdir(folder_path))
          ]
          target.extend(len(documents) * [label])
          filenames.extend(documents)
  
      # convert to array for fancy indexing
      filenames = np.array(filenames)
      target = np.array(target)
  
      if shuffle:
          random_state = check_random_state(random_state)
          indices = np.arange(filenames.shape[0])
          random_state.shuffle(indices)
          filenames = filenames[indices]
          target = target[indices]
  
      if load_content:
          data = []
          for filename in filenames:
              with open(filename, "rb") as f:
                  data.append(f.read())
          if encoding is not None:
              data = [d.decode(encoding, decode_error) for d in data]
          return Bunch(
              data=data,
              filenames=filenames,
              target_names=target_names,
              target=target,
              DESCR=description,
          )
  
      return Bunch(
          filenames=filenames,
          target_names=target_names,
          target=target,
          DESCR=description,
      )
  
imports: import os
  import csv
  import sys
  import shutil
  from os import environ
  from os.path import dirname
  from os.path import join
  from os.path import exists
  from os.path import expanduser
  from os.path import isdir
  from os.path import splitext
  from os import listdir
  from os import makedirs
  import numpy
  from ..utils import check_random_state
label_desc: Use image node_id 4 for calling a global function with example usage: load_files(container_path, description, categories, load_content, shuffle, encoding, decode_error, random_state) and returns: Bunch, Bunch