docstr: Compute cosine similarity between two post-bulk out of training documents.
  
  Parameters
  ----------
  model : :class:`~gensim.models.doc2vec.Doc2Vec`
      An instance of a trained `Doc2Vec` model.
  doc_words1 : list of str
      Input document.
  doc_words2 : list of str
      Input document.
  alpha : float, optional
      The initial learning rate.
  min_alpha : float, optional
      Learning rate will linearly drop to `min_alpha` as training progresses.
  epochs : int, optional
      Number of epoch to train the new document.
  
  Returns
  -------
  float
      The cosine similarity between `doc_words1` and `doc_words2`.
code: def similarity_unseen_docs(
      self,
      doc_words1,
      doc_words2,
      alpha=None,
      min_alpha=None,
      epochs=None,
  ):
      """Compute cosine similarity between two post-bulk out of training documents.
  
      Parameters
      ----------
      model : :class:`~gensim.models.doc2vec.Doc2Vec`
          An instance of a trained `Doc2Vec` model.
      doc_words1 : list of str
          Input document.
      doc_words2 : list of str
          Input document.
      alpha : float, optional
          The initial learning rate.
      min_alpha : float, optional
          Learning rate will linearly drop to `min_alpha` as training progresses.
      epochs : int, optional
          Number of epoch to train the new document.
  
      Returns
      -------
      float
          The cosine similarity between `doc_words1` and `doc_words2`.
  
      """
      d1 = self.infer_vector(
          doc_words=doc_words1,
          alpha=alpha,
          min_alpha=min_alpha,
          epochs=epochs,
      )
      d2 = self.infer_vector(
          doc_words=doc_words2,
          alpha=alpha,
          min_alpha=min_alpha,
          epochs=epochs,
      )
      return np.dot(matutils.unitvec(d1), matutils.unitvec(d2))
  
imports: import logging
  import os
  from collections import namedtuple, defaultdict
  from collections.abc import Iterable
  from timeit import default_timer
  from dataclasses import dataclass
  from numpy import zeros, float32, vstack, integer, dtype
  import numpy
  from gensim import utils, matutils
  from gensim.utils import deprecated
  from gensim.models import Word2Vec, FAST_VERSION
  from gensim.models.keyedvectors import KeyedVectors, pseudorandom_weak_vector
label_desc: Use image node_id 26 for calling the Doc2Vec obj's underlying member method code with example usage: obj.similarity_unseen_docs(doc_words1, doc_words2, alpha, min_alpha, epochs) and returns: np