code: def test_multiple_outputs(batch):
      """Test case for multiple outputs"""
      input1 = tf.keras.layers.Input(shape=(10,))
      inputs = [input1]
      output1 = tf.keras.layers.Dense(
          5, kernel_initializer="ones", use_bias=False
      )(input1)
      output2 = tf.keras.layers.Dense(
          2, kernel_initializer="ones", use_bias=False
      )(input1)
      outputs = [output1, output2]
      model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
  
      # create the ModelGraph
      config = hls4ml.utils.config_from_keras_model(
          model,
          granularity="model",
          default_precision="ap_fixed<32,16>",
      )
      odir = str(test_root_path / "hls4mlprj_graph_multiple_outputs")
      hls_model = hls4ml.converters.convert_from_keras_model(
          model,
          output_dir=odir,
          backend="Vivado",
          io_type="io_parallel",
          hls_config=config,
      )
      hls_model.compile()
  
      # Test with integers (for exact agreement)
      X1 = np.random.randint(0, 100, size=(batch, 10)).astype(float)
      y = model.predict(X1)
      y_hls = hls_model.predict(X1)
      # test trace as well
      y_hls, hls_trace = hls_model.trace(X1)
      for y_i, y_hls_i in zip(y, y_hls):
          y_hls_i = y_hls_i.reshape(y_i.shape)
          np.testing.assert_allclose(y_i, y_hls_i, rtol=0)
  
file_path: hls4ml/test/pytest/test_graph.py
imports: from pathlib import Path
  import numpy
  import pytest
  import tensorflow
  import hls4ml
label_desc: Use image node_id 11 for calling a global function with example usage: test_multiple_outputs(batch) without return types