class_docstr: Wrapper for self-hosted Word2Vec embeddings.
class_docstr_tok: ["Wrapper","for","self-hosted","Word2Vec","embeddings","."]
code: def __init__(
      self,
      iso_code: str,
      model_type: str = "txt",
      interactive: bool = True,
      silent: bool = False,
      overwrite: bool = False,
  ):
      self.iso_code = iso_code
      self.model_type = model_type
      self.interactive = interactive
      self.silent = silent
      self.overwrite = overwrite
  
      if self.interactive and self.silent:
          raise ValueError(
              "``interactive`` and ``silent`` options are not compatible with each other."
          )
  
      self._check_input_params()
  
      self.model_path = make_cltk_path(
          self.iso_code,
          "model",
          f"{self.iso_code}_models_cltk",
          "semantics",
      )
  
      # load model after all checks OK
      self.fp_model = self._build_filepath()
      if not self._is_model_present() or self.overwrite:
          self._download_cltk_self_hosted_models()
      elif self._is_model_present() and not self.overwrite:
          message = (
              f"Model for '{self.iso_code}' / '{self.model_type}' already present "
              f"at '{self.fp_model}' and ``overwrite=False``."
          )
          logger.info(message)
      self.model: models.word2vec.Word2Vec = self._load_model()
  
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 1 to create a new CLTKWord2VecEmbeddings object  with example: obj = CLTKWord2VecEmbeddings(iso_code, model_type, interactive, silent, overwrite)