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 __init__(
      self,
      output_sequence_length: Optional[int] = None,
      max_tokens: int = 20000,
      **kwargs,
  ):
      super().__init__(**kwargs)
      self.output_sequence_length = output_sequence_length
      self.max_tokens = max_tokens
  
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 1 to create a new TextToIntSequence object from inherited base classes: block_module with example: obj = TextToIntSequence(output_sequence_length, max_tokens)