class_docstr: APIs to help create and train an Average Word Embedding text classifier.
class_docstr_tok: ["APIs","to","help","create","and","train","an","Average","Word","Embedding","text","classifier","."]
docstr: Creates, trains, and returns an Average Word Embedding classifier.
  
  Args:
    train_data: Training data.
    validation_data: Validation data.
    options: Options for creating and training the text classifier.
  
  Returns:
    An Average Word Embedding classifier.
code: def create_average_word_embedding_classifier(
      cls,
      train_data: text_ds.Dataset,
      validation_data: text_ds.Dataset,
      options: text_classifier_options.TextClassifierOptions,
  ) -> "_AverageWordEmbeddingClassifier":
      """Creates, trains, and returns an Average Word Embedding classifier.
  
      Args:
        train_data: Training data.
        validation_data: Validation data.
        options: Options for creating and training the text classifier.
  
      Returns:
        An Average Word Embedding classifier.
      """
      average_word_embedding_classifier = (
          _AverageWordEmbeddingClassifier(
              model_spec=options.supported_model.value(),
              model_options=options.model_options,
              hparams=options.hparams,
              label_names=train_data.label_names,
          )
      )
      average_word_embedding_classifier._create_and_train_model(
          train_data, validation_data
      )
      return average_word_embedding_classifier
  
imports: import abc
  import os
  import tempfile
  from typing import Any, Optional, Sequence, Tuple
  import tensorflow
  from tensorflow_addons import optimizers
  import tensorflow_hub
  from mediapipe.model_maker.python.core.data import dataset
  from mediapipe.model_maker.python.core.tasks import classifier
  from mediapipe.model_maker.python.core.utils import hub_loader
  from mediapipe.model_maker.python.core.utils import loss_functions
  from mediapipe.model_maker.python.core.utils import metrics
  from mediapipe.model_maker.python.core.utils import model_util
  from mediapipe.model_maker.python.core.utils import quantization
  from mediapipe.model_maker.python.text.text_classifier import bert_tokenizer
  from mediapipe.model_maker.python.text.text_classifier import dataset
  from mediapipe.model_maker.python.text.text_classifier import hyperparameters
  from mediapipe.model_maker.python.text.text_classifier import model_options
  from mediapipe.model_maker.python.text.text_classifier import model_spec
  from mediapipe.model_maker.python.text.text_classifier import model_with_tokenizer
  from mediapipe.model_maker.python.text.text_classifier import preprocessor
  from mediapipe.model_maker.python.text.text_classifier import text_classifier_options
  from mediapipe.tasks.python.metadata.metadata_writers import metadata_writer
  from mediapipe.tasks.python.metadata.metadata_writers import text_classifier
label_desc: Use image node_id 2 for calling the _AverageWordEmbeddingClassifier obj's underlying member method code with example usage: obj.create_average_word_embedding_classifier(cls, train_data, validation_data, options) and returns: average_word_embedding_classifier