docstr: Parameters
  ----------
  epochs : int
    Number of epochs to train for.  Default is 2000.
  super_epochs : int
      Number of times to repeat training process. Default is training_iterations.
  
  Returns
  -------
  A trained word2vec model.
docstr_tok: ["Parameters","--","--","--","--","--","epochs",":","int","Number","of","epochs","to","train","for",".","Default","is","2000.","super_epochs",":","int","Number","of","times","to","repeat","training","process",".","Default","is","training_iterations",".","Returns","--","--","--","-","A","trained","word2vec","model","."]
code: def train(self, epochs=2000, super_epochs=5, tqdm=None):
      """
      Parameters
      ----------
      epochs : int
        Number of epochs to train for.  Default is 2000.
      super_epochs : int
          Number of times to repeat training process. Default is training_iterations.
  
      Returns
      -------
      A trained word2vec model.
      """
  
      self._scan_and_build_vocab()
      myiter = range(super_epochs)
      if tqdm:
          myiter = tqdm(myiter, total=super_epochs)
      for _ in myiter:
          self.model.train(
              CorpusAdapterForGensim.get_sentences(self.corpus),
              total_examples=self.model.corpus_count,
              epochs=epochs,
          )
      return self.model
  
imports: import itertools
  import warnings
  from collections import Counter
  from scattertext.ParsedCorpus import ParsedCorpus
label_desc: Use image node_id 2 for calling the Word2VecFromParsedCorpus obj's underlying member method code with example usage: obj.train(epochs, super_epochs, tqdm) and returns: self