code: def fit(self, train_data: InputData):
      features_list = self._convert_to_one_dim(train_data.features)
  
      with ImplementationRandomStateHandler(
          implementation=self.vectorizer
      ):
          self.vectorizer.fit(features_list)
  
      return self.vectorizer
  
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 2 for calling the SkLearnTextVectorizeStrategy obj's underlying member method code with example usage: obj.fit(train_data) and returns: self