class_docstr: Wrapper for self-hosted Word2Vec embeddings.
class_docstr_tok: ["Wrapper","for","self-hosted","Word2Vec","embeddings","."]
docstr: Load model into memory.
code: def _load_model(self) -> models.word2vec.Word2Vec:
      """Load model into memory."""
      try:
          return models.word2vec.Word2Vec.load(
              os.path.join(
                  self.model_path, os.path.basename(self.fp_model)
              )
          )
      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 9 for calling the CLTKWord2VecEmbeddings obj's underlying member method code with example usage: obj._load_model() and returns: models