class_docstr: 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_dim=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)
      )
      six.assertCountEqual(
          self,
          expected_input_names,
          [x.name for x in spec.description.input],
      )
      self.assertEquals(
          len(spec.description.output), len(expected_output_names)
      )
      self.assertEquals(
          expected_output_names,
          [x.name for x in spec.description.output],
      )
  
imports: import unittest
  from coremltools._deps import _HAS_KERAS_TF
  from coremltools.proto import FeatureTypes_pb2
  import pytest
  import six
label_desc: Use image node_id 25 for calling the KerasSingleLayerTest obj's underlying member method code with example usage: obj.test_default_interface_names() without return types