code: def __init__(
      self,
      operation_type: str,
      params: Optional[OperationParameters] = None,
  ):
      super().__init__(operation_type, params)
      self.vectorizer = self._convert_to_operation(operation_type)
      self.vectorizer = self.vectorizer(self.params_for_fit)
  
imports: import warnings
  from typing import Optional
  import numpy
  from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
  from fedot.core.data.data import InputData, OutputData
  from fedot.core.operations.evaluation.evaluation_interfaces import EvaluationStrategy
  from fedot.core.operations.evaluation.operation_implementations.data_operations.text_preprocessing import TextCleanImplementation
  from fedot.core.operations.evaluation.operation_implementations.data_operations.text_pretrained import PretrainedEmbeddingsImplementation
  from fedot.core.operations.operation_parameters import OperationParameters
  from fedot.utilities.random import ImplementationRandomStateHandler
label_desc: Use image node_id 1 to create a new GensimTextVectorizeStrategy object from inherited base classes: EvaluationStrategy with example: obj = GensimTextVectorizeStrategy(operation_type, params)