class_docstr: Convert raw texts to sequences of word indices.
  
  # Arguments
      output_sequence_length: Int. The maximum length of a sentence. If
          unspecified, it would be tuned automatically.
      max_tokens: Int. The maximum size of the vocabulary. Defaults to 20000.
class_docstr_tok: ["Convert","raw","texts","to","sequences","of","word","indices",".","#","Arguments","output_sequence_length",":","Int",".","The","maximum","length","of","a","sentence",".","If","unspecified",",","it","would","be","tuned","automatically",".","max_tokens",":","Int",".","The","maximum","size","of","the","vocabulary",".","Defaults","to","20000","."]
code: def build(self, hp, inputs=None):
      input_node = nest.flatten(inputs)[0]
      if self.output_sequence_length is not None:
          output_sequence_length = self.output_sequence_length
      else:
          output_sequence_length = hp.Choice(
              "output_sequence_length", [64, 128, 256, 512], default=64
          )
      output_node = layers.TextVectorization(
          max_tokens=self.max_tokens,
          output_mode="int",
          output_sequence_length=output_sequence_length,
      )(input_node)
      return output_node
  
imports: from typing import Optional
  from typing import Tuple
  from typing import Union
  from keras_tuner.engine import hyperparameters
  from tensorflow import nest
  from tensorflow.keras import layers
  from autokeras import analysers
  from autokeras import keras_layers
  from autokeras.engine import block
  from autokeras.utils import io_utils
  from autokeras.utils import utils
label_desc: Use image node_id 3 for calling the TextToIntSequence obj's underlying member method code with example usage: obj.build(hp, inputs) and returns: output_node