class_docstr: Unit test class for testing scikit-learn converter.
class_docstr_tok: ["Unit","test","class","for","testing","scikit-learn","converter","."]
code: def test_default_interface_names(self):
      from keras.layers import Dense
      from keras.layers import Activation
  
      # Create a simple Keras model
      model = Sequential()
      model.add(Dense(32, input_shape=(16,)))
      model.add(Activation("softmax"))
  
      expected_input_names = ["input1"]
      expected_output_names = ["output1"]
      spec = keras.convert(model).get_spec()
      self.assertIsNotNone(spec)
  
      # Test the model class
      self.assertIsNotNone(spec.description)
      self.assertTrue(spec.HasField("neuralNetwork"))
  
      # Test the inputs and outputs
      self.assertEquals(
          len(spec.description.input), len(expected_input_names)
      )
      self.assertEqual(
          sorted(expected_input_names),
          sorted(map(lambda x: x.name, spec.description.input)),
      )
      self.assertEquals(
          len(spec.description.output), len(expected_output_names)
      )
      self.assertEquals(
          sorted(expected_output_names),
          sorted(map(lambda x: x.name, spec.description.output)),
      )
  
imports: import unittest
  from coremltools._deps import _HAS_KERAS2_TF
  from coremltools.proto import Model_pb2
  from coremltools.proto import FeatureTypes_pb2
  from coremltools.proto import NeuralNetwork_pb2
  import pytest
label_desc: Use image node_id 29 for calling the KerasSingleLayerTest obj's underlying member method code with example usage: obj.test_default_interface_names() without return types