docstr: Whether to use 'object' features identified as 'text' features to generate 'text_special' features such as word count, capital letter ratio, and symbol counts.
  Appends TextSpecialFeatureGenerator() to the generator group.
docstr_tok: ["Whether","to","use","'object","'","features","identified","as","'text","'","features","to","generate","'text_special","'","features","such","as","word","count",",","capital","letter","ratio",",","and","symbol","counts",".","Appends","TextSpecialFeatureGenerator","(",")","to","the","generator","group","."]
code: def enable_text_special_features(
      self, value: bool = True
  ) -> FeatureGeneratorBuilder:
      """
      Whether to use 'object' features identified as 'text' features to generate 'text_special' features such as word count, capital letter ratio, and symbol counts.
      Appends TextSpecialFeatureGenerator() to the generator group.
      """
      self.config["enable_text_special_features"] = 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 5 for calling the FeatureGeneratorBuilder obj's underlying member method code with example usage: obj.enable_text_special_features(value) and returns: self