class_docstr: Preprocessor for an Average Word Embedding model.
  
  Takes (text, label) data and applies regex tokenization and padding to the
  text to generate (token IDs, label) data.
  
  Attributes:
    seq_len: Length of the input sequence to the model.
    do_lower_case: Whether text inputs should be converted to lower-case.
    vocab: Vocabulary of tokens used by the model.
class_docstr_tok: ["Preprocessor","for","an","Average","Word","Embedding","model",".","Takes","(","text",",","label",")","data","and","applies","regex","tokenization","and","padding","to","the","text","to","generate","(","token","IDs",",","label",")","data",".","Attributes",":","seq_len",":","Length","of","the","input","sequence","to","the","model",".","do_lower_case",":","Whether","text","inputs","should","be","converted","to","lower-case",".","vocab",":","Vocabulary","of","tokens","used","by","the","model","."]
docstr: Returns the vocab of the AverageWordEmbeddingClassifierPreprocessor.
docstr_tok: ["Returns","the","vocab","of","the","AverageWordEmbeddingClassifierPreprocessor","."]
code: def get_vocab(self) -> Mapping[str, int]:
      """Returns the vocab of the AverageWordEmbeddingClassifierPreprocessor."""
      return self._vocab
  
imports: import collections
  import hashlib
  import os
  import re
  from typing import Mapping, Sequence, Tuple, Union
  import tensorflow
  import tensorflow_hub
  from mediapipe.model_maker.python.core.data import cache_files
  from mediapipe.model_maker.python.text.text_classifier import bert_tokenizer
  from mediapipe.model_maker.python.text.text_classifier import dataset
label_desc: Use image node_id 3 for calling the AverageWordEmbeddingClassifierPreprocessor obj's underlying member method code with example usage: obj.get_vocab() and returns: self