code: def main():
      np.random.seed(123)
      inp_a = np.random.uniform(size=(5, 10))
      inp_b = np.random.uniform(size=(5, 10))
      out_d = np.random.uniform(size=(5, 1))
      out_e = np.random.uniform(size=(5, 1))
  
      # Just for visual purposes.
      np.set_printoptions(precision=2)
  
      # Activations of all the layers
      print("MULTI-INPUT OUTPUT MODEL")
      m1 = get_multi_outputs_model()
      m1.compile(optimizer="adam", loss="mse")
      m1.fit(x=[inp_a, inp_b], y=[out_d, out_e])
  
      utils.print_names_and_values(
          keract.get_activations(m1, [inp_a, inp_b])
      )
  
file_path: keract/examples/multi_outputs.py
imports: import numpy
  import tensorflow
  from tensorflow.keras.layers import Add, Dense, Input
  from tensorflow.keras.models import Model
  import keract
  import utils
label_desc: Use image node_id 2 for calling a global function with example usage: main() without return types