docstr: Estimate required memory for a model using current settings.
  
  Parameters
  ----------
  vocab_size : int, optional
      Number of raw words in the vocabulary.
  report : dict of (str, int), optional
      A dictionary from string representations of the **specific** model's memory consuming members
      to their size in bytes.
  
  Returns
  -------
  dict of (str, int), optional
      A dictionary from string representations of the model's memory consuming members to their size in bytes.
      Includes members from the base classes as well as weights and tag lookup memory estimation specific to the
      class.
code: def estimate_memory(self, vocab_size=None, report=None):
      """Estimate required memory for a model using current settings.
  
      Parameters
      ----------
      vocab_size : int, optional
          Number of raw words in the vocabulary.
      report : dict of (str, int), optional
          A dictionary from string representations of the **specific** model's memory consuming members
          to their size in bytes.
  
      Returns
      -------
      dict of (str, int), optional
          A dictionary from string representations of the model's memory consuming members to their size in bytes.
          Includes members from the base classes as well as weights and tag lookup memory estimation specific to the
          class.
  
      """
      report = report or {}
      report["doctag_lookup"] = self.estimated_lookup_memory()
      report["doctag_syn0"] = (
          len(self.dv) * self.vector_size * dtype(REAL).itemsize
      )
      return super(Doc2Vec, self).estimate_memory(
          vocab_size, report=report
      )
  
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 21 for calling the Doc2Vec obj's underlying member method code with example usage: obj.estimate_memory(vocab_size, report) and returns: super