docstr: Parameters
  ----------
  corpus, ParsedCorpus
  category, str
  
  Returns
  -------
  iter: [sentence1word1, ...], [sentence2word1, ...]
docstr_tok: ["Parameters","--","--","--","--","--","corpus",",","ParsedCorpus","category",",","str","Returns","--","--","--","-","iter",":","[","sentence1word1",",","...","]",",","[","sentence2word1",",","...","]"]
code: def get_sentences(corpus, category):
      """
      Parameters
      ----------
      corpus, ParsedCorpus
      category, str
  
      Returns
      -------
      iter: [sentence1word1, ...], [sentence2word1, ...]
      """
      # assert isinstance(corpus, ParsedCorpus)
      return itertools.chain(
          *[
              [
                  [t.lower_ for t in sent if not t.is_punct]
                  for sent in doc.sents
              ]
              for doc_catgory, doc in zip(
                  corpus.get_category_names_by_row(),
                  corpus.get_parsed_docs(),
              )
              if category == doc_catgory
          ]
      )
  
imports: import itertools
  import re
  import warnings
  import numpy
  import pandas
  import scipy
  from sklearn.metrics.pairwise import cosine_similarity
  from scattertext.ParsedCorpus import ParsedCorpus
  from scattertext.representations.Word2VecFromParsedCorpus import Word2VecDefault
label_desc: Use image node_id 1 for calling the CartegorySpecificCorpusAdapterForGensim obj's underlying member method code with example usage: obj.get_sentences(corpus, category) and returns: itertools