docstr: Parameters
  ----------
  epochs : int
    Number of epochs to train for.  Default is 2000.
  training_iterations : int
      Number of times to repeat training process. Default is super_epochs.
  
  Returns
  -------
  A trained word2vec model.
docstr_tok: ["Parameters","--","--","--","--","--","epochs",":","int","Number","of","epochs","to","train","for",".","Default","is","2000.","training_iterations",":","int","Number","of","times","to","repeat","training","process",".","Default","is","super_epochs",".","Returns","--","--","--","-","A","trained","word2vec","model","."]
code: def train(self, epochs=2000, training_iterations=5):
      """
      Parameters
      ----------
      epochs : int
        Number of epochs to train for.  Default is 2000.
      training_iterations : int
          Number of times to repeat training process. Default is super_epochs.
  
      Returns
      -------
      A trained word2vec model.
      """
  
      self._scan_and_build_vocab()
      for _ in range(training_iterations):
          self.model.train(
              CartegorySpecificCorpusAdapterForGensim.get_sentences(
                  self.corpus, self.category
              ),
              total_examples=self.model.corpus_count,
              epochs=epochs,
          )
      return self.model
  
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 2 for calling the CategorySpecificWord2VecFromParsedCorpus obj's underlying member method code with example usage: obj.train(epochs, training_iterations) and returns: self