I'm trying to understand the PV-DM implementation with averaging in gensim.In the function train_document_dm in doc2vec.py the return value (errors) of train_cbow_pair is in the case of averaging (cbow_mean=1) not divided by the number of input vectors (count).According to this explanation there should be a division by the number of documents in the case of averaging the input vectors: word2vec Parameter Learning Explained, equation (23).Here is the code from train_document_dm:l1 = np_sum(word_vectors[word2_indexes], axis=0)+np_sum(doctag_vectors[doctag_indexes], axis=0)  count = len(word2_indexes) + len(doctag_indexes)  if model.cbow_mean and count > 1:      l1 /= count  neu1e = train_cbow_pair(model, word, word2_indexes, l1, alpha,                                learn_vectors=False,  learn_hidden=learn_hidden)  if not model.cbow_mean and count > 1:      neu1e /= count  if learn_doctags:      for i in doctag_indexes:          doctag_vectors[i] += neu1e * doctag_locks[i]  if learn_words:      for i in word2_indexes:          word_vectors[i] += neu1e * word_locks[i]  
updates of the document vectors in doc2vec (PV-DM) in gensim
python;word2vec;gensim