code: def test_triplet_network():
      X = np.zeros(shape=(10, 5))
      embedding_dims = 3
  
      base_model = Sequential()
      base_model.add(Dense(8, input_shape=(X.shape[-1],)))
  
      model, _ = triplet_network(
          base_model, embedding_dims=embedding_dims, embedding_l2=0.1
      )
      encoder = model.layers[3]
  
      assert model.layers[3].output_shape == (None, 3)
      assert np.all(
          base_model.get_weights()[0] == encoder.get_weights()[0]
      )
      assert np.all(
          [
              isinstance(layer, keras.layers.InputLayer)
              for layer in model.layers[:3]
          ]
      )
  
      assert encoder.output_shape == (None, embedding_dims)
  
imports: from tensorflow import keras
  from tensorflow.keras.models import Model, Sequential
  from tensorflow.keras.layers import Dense
  import numpy
  from ivis.nn.network import get_base_networks, base_network, triplet_network
label_desc: Use image node_id 2 for calling a global function with example usage: test_triplet_network() without return types