class_docstr: Wrapper for Word2Vec embeddings. Note: For models
  provided by fastText, use class ``FastTextEmbeddings``.
class_docstr_tok: ["Wrapper","for","Word2Vec","embeddings",".","Note",":","For","models","provided","by","fastText",",","use","class","``","FastTextEmbeddings","``","."]
docstr: Load model into memory.
  
  TODO: When testing show that this is a Gensim type
  TODO: Suppress Gensim info printout from screen
code: def _load_model(self) -> models.keyedvectors.Word2VecKeyedVectors:
      """Load model into memory.
  
      TODO: When testing show that this is a Gensim type
      TODO: Suppress Gensim info printout from screen
      """
      # KJ added these two checks because NLPL embeddings
      # began erring in Gensim (Oct 2021)
      is_binary: bool = False
      unicode_errors: str = "strict"
      if self.fp_model.endswith(".txt"):
          unicode_errors = "ignore"
      if self.fp_model.endswith(".bin"):
          is_binary = True
      try:
          return models.KeyedVectors.load_word2vec_format(
              self.fp_model,
              binary=is_binary,
              unicode_errors=unicode_errors,
          )
      except UnicodeDecodeError:
          msg = f"Cannot open file '{self.fp_model}' with Gensim 'load_word2vec_format'."
          print(msg)
          raise UnicodeDecodeError
  
imports: import os
  from typing import List
  from zipfile import ZipFile
  from gensim import models
  from cltk.core.cltk_logger import logger
  from cltk.core.exceptions import CLTKException, UnimplementedAlgorithmError
  from cltk.data.fetch import FetchCorpus
  from cltk.languages.utils import get_lang
  from cltk.utils import CLTK_DATA_DIR, get_file_with_progress_bar, query_yes_no
  from cltk.utils.file_operations import make_cltk_path
label_desc: Use image node_id 11 for calling the Word2VecEmbeddings obj's underlying member method code with example usage: obj._load_model() and returns: models