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 transformation of the text data for predict stage
  
  :param input_data: data with features, target and ids to process
  :return output_data: output data with transformed features table
docstr_tok: ["Method","for","transformation","of","the","text","data","for","predict","stage",":","param","input_data",":","data","with","features",",","target","and","ids","to","process",":","return","output_data",":","output","data","with","transformed","features","table"]
code: def transform(self, input_data: InputData) -> OutputData:
      """Method for transformation of the text data for predict stage
  
      :param input_data: data with features, target and ids to process
      :return output_data: output data with transformed features table
      """
  
      embed_data = np.stack(
          [
              self.vectorize_avg(text, self.model)
              for text in input_data.features
          ]
      )
      output_data = self._convert_to_output(
          input_data, embed_data, data_type=DataTypesEnum.table
      )
      return output_data
  
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 3 for calling the PretrainedEmbeddingsImplementation obj's underlying member method code with example usage: obj.transform(input_data) and returns: output_data