tensorflow_examples/lite/model_maker/core/task/model_spec/text_spec.py [257:292]:
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                            max_seq_length,
                            initializer=None,
                            hub_module_url=None,
                            hub_module_trainable=True,
                            is_tf2=True):
  """BERT classifier model in functional API style.

  Construct a Keras model for predicting `num_labels` outputs from an input with
  maximum sequence length `max_seq_length`.

  Args:
    bert_config: BertConfig, the config defines the core Bert model.
    num_labels: integer, the number of classes.
    max_seq_length: integer, the maximum input sequence length.
    initializer: Initializer for the final dense layer in the span labeler.
      Defaulted to TruncatedNormal initializer.
    hub_module_url: TF-Hub path/url to Bert module.
    hub_module_trainable: True to finetune layers in the hub module.
    is_tf2: boolean, whether the hub module is in TensorFlow 2.x format.

  Returns:
    Combined prediction model (words, mask, type) -> (one-hot labels)
    BERT sub-model (words, mask, type) -> (bert_outputs)
  """
  if initializer is None:
    initializer = tf.keras.initializers.TruncatedNormal(
        stddev=bert_config.initializer_range)

  input_word_ids = tf.keras.layers.Input(
      shape=(max_seq_length,), dtype=tf.int32, name='input_word_ids')
  input_mask = tf.keras.layers.Input(
      shape=(max_seq_length,), dtype=tf.int32, name='input_mask')
  input_type_ids = tf.keras.layers.Input(
      shape=(max_seq_length,), dtype=tf.int32, name='input_type_ids')

  if is_tf2:
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tensorflow_examples/lite/model_maker/core/task/model_spec/text_spec.py [576:608]:
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                    max_seq_length,
                    initializer=None,
                    hub_module_url=None,
                    hub_module_trainable=True,
                    is_tf2=True):
  """Returns BERT qa model along with core BERT model to import weights.

  Args:
    bert_config: BertConfig, the config defines the core Bert model.
    max_seq_length: integer, the maximum input sequence length.
    initializer: Initializer for the final dense layer in the span labeler.
      Defaulted to TruncatedNormal initializer.
    hub_module_url: TF-Hub path/url to Bert module.
    hub_module_trainable: True to finetune layers in the hub module.
    is_tf2: boolean, whether the hub module is in TensorFlow 2.x format.

  Returns:
    A tuple of (1) keras model that outputs start logits and end logits and
    (2) the core BERT transformer encoder.
  """

  if initializer is None:
    initializer = tf.keras.initializers.TruncatedNormal(
        stddev=bert_config.initializer_range)

  input_word_ids = tf.keras.layers.Input(
      shape=(max_seq_length,), dtype=tf.int32, name='input_word_ids')
  input_mask = tf.keras.layers.Input(
      shape=(max_seq_length,), dtype=tf.int32, name='input_mask')
  input_type_ids = tf.keras.layers.Input(
      shape=(max_seq_length,), dtype=tf.int32, name='input_type_ids')

  if is_tf2:
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