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: Constructor for  ``Word2VecEmbeddings`` class.
code: def __init__(
      self,
      iso_code: str,
      model_type: str = "txt",
      interactive: bool = True,
      silent: bool = False,
      overwrite: bool = False,
  ):
      """Constructor for  ``Word2VecEmbeddings`` class."""
      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()
  
      # load model after all checks OK
      self.fp_zip = self._build_zip_filepath()
      self.fp_model = self._build_nlpl_filepath()
      self.fp_model_dirs = os.path.split(self.fp_zip)[0]  # type: str
      if not self._is_nlpl_model_present() or self.overwrite:
          self._download_nlpl_models()
          self._unzip_nlpl_model()
      elif self._is_nlpl_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)
          pass
      self.model: models.keyedvectors.Word2VecKeyedVectors = (
          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 Word2VecEmbeddings object  with example: obj = Word2VecEmbeddings(iso_code, model_type, interactive, silent, overwrite)