code: def get_small_bert_encoder_and_sample_inputs(dict_outputs=False):
      """Returns a small BERT encoder for testing."""
      hidden_size = 2
      vocab_size = 3
      num_types = 4
      max_sequence_length = 5
      inner_dense_units = 6
      output_range = 1
      num_heads = 2
      num_transformer_layers = 3
      seed = 777
  
      bert_encoder = tfm.nlp.networks.BertEncoder(
          vocab_size=vocab_size,
          hidden_size=hidden_size,
          num_attention_heads=num_heads,
          num_layers=num_transformer_layers,
          max_sequence_length=max_sequence_length,
          inner_dim=inner_dense_units,
          type_vocab_size=num_types,
          output_range=output_range,
          initializer=tf.keras.initializers.GlorotUniform(seed),
          dict_outputs=dict_outputs,
      )
  
      batch_size = 3
      bert_sample_inputs = compute_bert_sample_inputs(
          batch_size,
          max_sequence_length,
          vocab_size,
          num_types,
      )
  
      return bert_encoder, bert_sample_inputs
  
file_path: privacy/tensorflow_privacy/privacy/fast_gradient_clipping/bert_encoder_utils_test.py
imports: from absl.testing import parameterized
  import numpy
  import tensorflow
  import tensorflow_models
  from tensorflow_privacy.privacy.fast_gradient_clipping import bert_encoder_utils
label_desc: Use image node_id 2 for calling a global function with example usage: get_small_bert_encoder_and_sample_inputs(dict_outputs) and returns: bert_encoder,  bert_sample_inputs