code: def _get_transformer_encoder(
      bert_config, sequence_length, float_dtype=tf.float32
  ):
      """Gets a 'TransformerEncoder' object.
  
      Args:
        bert_config: A 'modeling.BertConfig' object.
        sequence_length: Maximum sequence length of the training data.
        float_dtype: tf.dtype, tf.float32 or tf.float16.
  
      Returns:
        A networks.TransformerEncoder object.
      """
      return networks.TransformerEncoder(
          vocab_size=bert_config.vocab_size,
          hidden_size=bert_config.hidden_size,
          num_layers=bert_config.num_hidden_layers,
          num_attention_heads=bert_config.num_attention_heads,
          intermediate_size=bert_config.intermediate_size,
          activation=tf_utils.get_activation("gelu"),
          dropout_rate=bert_config.hidden_dropout_prob,
          attention_dropout_rate=bert_config.attention_probs_dropout_prob,
          sequence_length=sequence_length,
          max_sequence_length=bert_config.max_position_embeddings,
          type_vocab_size=bert_config.type_vocab_size,
          initializer=tf.keras.initializers.TruncatedNormal(
              stddev=bert_config.initializer_range
          ),
          float_dtype=float_dtype.name,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  import tensorflow_hub
  from utils import tf_utils
  from utils.modeling import losses
  from utils.modeling import networks
  from utils.modeling.networks import bert_classifier
  from utils.modeling.networks import bert_pretrainer
  from utils.modeling.networks import bert_span_labeler
label_desc: Use image node_id 2 for calling a global function with example usage: _get_transformer_encoder(bert_config, sequence_length, float_dtype) and returns: networks