How to use the tensorflowonspark.pipeline.Namespace function in tensorflowonspark

def test_namespace(self):
    """Namespace class initializers"""
    # from dictionary
    d = {'string': 'foo', 'integer': 1, 'float': 3.14, 'array': [1, 2, 3], 'map': {'a': 1, 'b': 2}}
    n1 = Namespace(d)
    self.assertEqual(n1.string, 'foo')
    self.assertEqual(n1.integer, 1)
    self.assertEqual(n1.float, 3.14)
    self.assertEqual(n1.array, [1, 2, 3])
    self.assertEqual(n1.map, {'a': 1, 'b': 2})
    self.assertTrue('string' in n1)
    self.assertFalse('extra' in n1)

    # from namespace
    n2 = Namespace(n1)
    self.assertEqual(n2.string, 'foo')
    self.assertEqual(n2.integer, 1)
    self.assertEqual(n2.float, 3.14)
    self.assertEqual(n2.array, [1, 2, 3])
    self.assertEqual(n2.map, {'a': 1, 'b': 2})
    self.assertTrue('string' in n2)
    self.assertFalse('extra' in n2)

    # from argv list
    argv = ["--foo", "1", "--bar", "test", "--baz", "3.14"]
    n3 = Namespace(argv)
    self.assertEqual(n3.argv, argv)

def test_TFParams(self):
    """Merging namespace args w/ ML Params"""
    class Foo(TFParams, HasBatchSize, HasSteps):
      def __init__(self, args):
        super(Foo, self).__init__()
        self.args = args

    n = Namespace({'a': 1, 'b': 2})
    f = Foo(n).setBatchSize(10).setSteps(100)
    combined_args = f.merge_args_params()
    expected_args = Namespace({'a': 1, 'b': 2, 'batch_size': 10, 'steps': 100})
    self.assertEqual(combined_args, expected_args)

self.assertTrue('string' in n1)
    self.assertFalse('extra' in n1)

    # from namespace
    n2 = Namespace(n1)
    self.assertEqual(n2.string, 'foo')
    self.assertEqual(n2.integer, 1)
    self.assertEqual(n2.float, 3.14)
    self.assertEqual(n2.array, [1, 2, 3])
    self.assertEqual(n2.map, {'a': 1, 'b': 2})
    self.assertTrue('string' in n2)
    self.assertFalse('extra' in n2)

    # from argv list
    argv = ["--foo", "1", "--bar", "test", "--baz", "3.14"]
    n3 = Namespace(argv)
    self.assertEqual(n3.argv, argv)

def test_TFParams(self):
    """Merging namespace args w/ ML Params"""
    class Foo(TFParams, HasBatchSize, HasSteps):
      def __init__(self, args):
        super(Foo, self).__init__()
        self.args = args

    n = Namespace({'a': 1, 'b': 2})
    f = Foo(n).setBatchSize(10).setSteps(100)
    combined_args = f.merge_args_params()
    expected_args = Namespace({'a': 1, 'b': 2, 'batch_size': 10, 'steps': 100})
    self.assertEqual(combined_args, expected_args)

def test_namespace(self):
    """Namespace class initializers"""
    # from dictionary
    d = {'string': 'foo', 'integer': 1, 'float': 3.14, 'array': [1, 2, 3], 'map': {'a': 1, 'b': 2}}
    n1 = Namespace(d)
    self.assertEqual(n1.string, 'foo')
    self.assertEqual(n1.integer, 1)
    self.assertEqual(n1.float, 3.14)
    self.assertEqual(n1.array, [1, 2, 3])
    self.assertEqual(n1.map, {'a': 1, 'b': 2})
    self.assertTrue('string' in n1)
    self.assertFalse('extra' in n1)

    # from namespace
    n2 = Namespace(n1)
    self.assertEqual(n2.string, 'foo')
    self.assertEqual(n2.integer, 1)
    self.assertEqual(n2.float, 3.14)
    self.assertEqual(n2.array, [1, 2, 3])
    self.assertEqual(n2.map, {'a': 1, 'b': 2})
    self.assertTrue('string' in n2)

def __init__(self, d):
    if isinstance(d, list):
      self.argv = d
    elif isinstance(d, dict):
      self.__dict__.update(d)
    elif isinstance(d, argparse.Namespace):
      self.__dict__.update(vars(d))
    elif isinstance(d, Namespace):
      self.__dict__.update(d.__dict__)
    else:
      raise Exception("Unsupported Namespace args: {}".format(d))

def __init__(self, train_fn, tf_args):
    super(TFEstimator, self).__init__()
    self.train_fn = train_fn
    self.args = Namespace(tf_args)
    self._setDefault(input_mapping={},
                     cluster_size=1,
                     num_ps=0,
                     driver_ps_nodes=False,
                     master_node='chief',
                     protocol='grpc',
                     tensorboard=False,
                     model_dir=None,
                     export_dir=None,
                     tfrecord_dir=None,
                     batch_size=100,
                     epochs=1,
                     readers=1,
                     steps=1000,
                     grace_secs=30)

def __init__(self, tf_args):
    super(TFModel, self).__init__()
    self.args = Namespace(tf_args)
    self._setDefault(input_mapping={},
                     output_mapping={},
                     batch_size=100,
                     model_dir=None,
                     export_dir=None,
                     signature_def_key='serving_default',
                     tag_set='serve')