docstr: Train a single batch of sentences. Return 2-tuple `(effective word count after
  ignoring unknown words and sentence length trimming, total word count)`.
  
  Parameters
  ----------
  sentences : iterable of list of str
      Can be simply a list of lists of tokens, but for larger corpora,
      consider an iterable that streams the sentences directly from disk/network.
      See :class:`~gensim.models.word2vec.BrownCorpus`, :class:`~gensim.models.word2vec.Text8Corpus`
      or :class:`~gensim.models.word2vec.LineSentence` in :mod:`~gensim.models.word2vec` module for such examples.
  alpha : float
      The current learning rate.
  inits : tuple of (:class:`numpy.ndarray`, :class:`numpy.ndarray`)
      Each worker's private work memory.
  
  Returns
  -------
  (int, int)
      Tuple of (effective word count after ignoring unknown words and sentence length trimming, total word count)
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code: def _do_train_job(self, sentences, alpha, inits):
      """Train a single batch of sentences. Return 2-tuple `(effective word count after
      ignoring unknown words and sentence length trimming, total word count)`.
  
      Parameters
      ----------
      sentences : iterable of list of str
          Can be simply a list of lists of tokens, but for larger corpora,
          consider an iterable that streams the sentences directly from disk/network.
          See :class:`~gensim.models.word2vec.BrownCorpus`, :class:`~gensim.models.word2vec.Text8Corpus`
          or :class:`~gensim.models.word2vec.LineSentence` in :mod:`~gensim.models.word2vec` module for such examples.
      alpha : float
          The current learning rate.
      inits : tuple of (:class:`numpy.ndarray`, :class:`numpy.ndarray`)
          Each worker's private work memory.
  
      Returns
      -------
      (int, int)
          Tuple of (effective word count after ignoring unknown words and sentence length trimming, total word count)
  
      """
      work, neu1 = inits
      tally = train_batch_any(self, sentences, alpha, work, neu1)
  
      return tally, self._raw_word_count(sentences)
  
imports: import logging
  import numpy
  from numpy import ones, vstack, float32
  import gensim.models._fasttext_bin
  from gensim.models.word2vec import Word2Vec
  from gensim.models.keyedvectors import KeyedVectors, prep_vectors
  from gensim import utils
  from gensim.utils import deprecated
  from gensim.models import keyedvectors
label_desc: Use image node_id 6 for calling the FastText obj's underlying member method code with example usage: obj._do_train_job(sentences, alpha, inits) and returns: tally,  self