class_docstr: Block for text data.
  
  # Arguments
      block_type: String. 'vanilla', 'transformer', and 'ngram'. The type of
          Block to use. 'vanilla' and 'transformer' use a TextToIntSequence
          vectorizer, whereas 'ngram' uses TextToNgramVector. If unspecified,
          it will be tuned automatically.
      max_tokens: Int. The maximum size of the vocabulary.
          If left unspecified, it will be tuned automatically.
      pretraining: String. 'random' (use random weights instead any pretrained
          model), 'glove', 'fasttext' or 'word2vec'. Use pretrained word
          embedding.  If left unspecified, it will be tuned automatically.
code: def get_config(self):
      config = super().get_config()
      config.update(
          {
              BLOCK_TYPE: self.block_type,
              MAX_TOKENS: self.max_tokens,
              "pretraining": self.pretraining,
          }
      )
      return config
  
imports: from typing import Optional
  from tensorflow import nest
  from autokeras.blocks import basic
  from autokeras.blocks import preprocessing
  from autokeras.blocks import reduction
  from autokeras.engine import block
label_desc: Use image node_id 2 for calling the TextBlock obj's underlying member method code with example usage: obj.get_config() and returns: config