class_docstr: Combination for Keras model types when doing model test.
  
  It by default includes 'functional', 'subclass', 'sequential'.
  
  Various methods in `testing_utils` to get models will auto-generate a model
  of the currently active Keras model type. This allows unittests to confirm
  the equivalence between different Keras models.
class_docstr_tok: ["Combination","for","Keras","model","types","when","doing","model","test",".","It","by","default","includes","'functional","'",",","'subclass","'",",","'sequential","'",".","Various","methods","in","`","testing_utils","`","to","get","models","will","auto-generate","a","model","of","the","currently","active","Keras","model","type",".","This","allows","unittests","to","confirm","the","equivalence","between","different","Keras","models","."]
code: def parameter_modifiers(self):
      return [test_combinations.OptionalParameter("model_type")]
  
file_path: tensorflow-upstream/tensorflow/python/keras/combinations.py
imports: import functools
  from tensorflow.python import tf2
  from tensorflow.python.framework import combinations
  from tensorflow.python.framework import test_combinations
  from tensorflow.python.keras import testing_utils
label_desc: Use image node_id 2 for calling the KerasModelTypeCombination obj's underlying member method code with example usage: obj.parameter_modifiers() and returns: list