class_docstr: A collection of texts, which can be loaded with list of texts, or
  with a corpus consisting of one or more texts, and which supports
  counting, concordancing, collocation discovery, etc.  Initialize a
  TextCollection as follows:
  
  >>> import nltk.corpus
  >>> from nltk.text import TextCollection
  >>> from nltk.book import text1, text2, text3
  >>> gutenberg = TextCollection(nltk.corpus.gutenberg)
  >>> mytexts = TextCollection([text1, text2, text3])
  
  Iterating over a TextCollection produces all the tokens of all the
  texts in order.
code: def __init__(self, source):
      if hasattr(source, "words"):  # bridge to the text corpus reader
          source = [source.words(f) for f in source.fileids()]
  
      self._texts = source
      Text.__init__(self, LazyConcatenation(source))
      self._idf_cache = {}
  
imports: import re
  import sys
  import unicodedata
  from collections import Counter, defaultdict, namedtuple
  from functools import reduce
  from math import log
  from nltk.collocations import BigramCollocationFinder
  from nltk.lm import MLE
  from nltk.lm.preprocessing import padded_everygram_pipeline
  from nltk.metrics import BigramAssocMeasures, f_measure
  from nltk.probability import ConditionalFreqDist
  from nltk.probability import FreqDist
  from nltk.tokenize import sent_tokenize
  from nltk.util import LazyConcatenation, cut_string, tokenwrap
label_desc: Use image node_id 1 to create a new TextCollection object from inherited base classes: Text with example: obj = TextCollection(source)