class_docstr: Class for text vectorization by pretrained embeddings
  model_name can be selected from https://github.com/RaRe-Technologies/gensim-data
docstr: Method for downloading text embeddings. Embeddings are loaded into external folder
docstr_tok: ["Method","for","downloading","text","embeddings",".","Embeddings","are","loaded","into","external","folder"]
code: def _download_model_resources(self):
      """Method for downloading text embeddings. Embeddings are loaded into external folder"""
      self.logger.info("Trying to download embeddings...")
      model_name = self.params.setdefault(
          "model_name", "glove-twitter-25"
      )
      model_path = api.load(f"{model_name}", return_path=True)
  
      if os.path.exists(model_path):
          self.logger.info(
              "Embeddings are already downloaded. Loading model..."
          )
          self.model = KeyedVectors.load_word2vec_format(
              model_path, binary=False
          )
  
file_path: FEDOT/fedot/core/operations/evaluation/operation_implementations/data_operations/text_pretrained.py
imports: import os
  from typing import Optional
  import numpy
  from golem.core.log import default_log
  from golem.utilities.requirements_notificator import warn_requirement
  from fedot.core.data.data import InputData, OutputData
  from fedot.core.operations.evaluation.operation_implementations.implementation_interfaces import DataOperationImplementation
  from fedot.core.operations.operation_parameters import OperationParameters
  from fedot.core.repository.dataset_types import DataTypesEnum
label_desc: Use image node_id 5 for calling the PretrainedEmbeddingsImplementation obj's underlying member method code with example usage: obj._download_model_resources() without return types