docstr: sklearn CountVectorizer object to use in TextNgramFeatureGenerator.
  Only used if `enable_text_ngram_features=True`.
docstr_tok: ["sklearn","CountVectorizer","object","to","use","in","TextNgramFeatureGenerator",".","Only","used","if","`","enable_text_ngram_features=True","`","."]
code: def vectorizer(self, value: BaseEstimator) -> FeatureGeneratorBuilder:
      """
      sklearn CountVectorizer object to use in TextNgramFeatureGenerator.
      Only used if `enable_text_ngram_features=True`.
      """
      self.config["vectorizer"] = value
      return self
  
imports: from __future__ import annotations
  import copy
  from typing import Union
  from sklearn.base import BaseEstimator
  from autogluon.core.scheduler import scheduler_factory
  from autogluon.features import AutoMLPipelineFeatureGenerator
  from autogluon.tabular.configs.hyperparameter_configs import hyperparameter_config_dict
  from autogluon.tabular.configs.presets_configs import tabular_presets_dict
  from autogluon.tabular.trainer.model_presets.presets import MODEL_TYPES
label_desc: Use image node_id 9 for calling the FeatureGeneratorBuilder obj's underlying member method code with example usage: obj.vectorizer(value) and returns: self