Update TensorFlow Privacy to use Python 3 metaclass.

PiperOrigin-RevId: 424773127

tensorflow_privacy/privacy/dp_query/dp_query.py

@@ -47,7 +47,7 @@ import collections
 import tensorflow.compat.v1 as tf
 
 
-class DPQuery(object):
+class DPQuery(metaclass=abc.ABCMeta):
   """Interface for differentially private query mechanisms.
 
   Differential privacy is achieved by processing records to bound sensitivity,
@@ -93,8 +93,6 @@ class DPQuery(object):
   ```
   """
 
-  __metaclass__ = abc.ABCMeta
-
   def initial_global_state(self):
     """Returns the initial global state for the DPQuery.
 

tensorflow_privacy/privacy/dp_query/nested_query_test.py

@@ -120,7 +120,23 @@ class NestedQueryTest(tf.test.TestCase, parameterized.TestCase):
   def test_raises_with_non_sum(self):
 
     class NonSumDPQuery(dp_query.DPQuery):
-      pass
+
+      def initial_sample_state(self, template=None):
+        del template  # Unused.
+        return None
+
+      def accumulate_preprocessed_record(self, sample_state,
+                                         preprocessed_record):
+        del sample_state, preprocessed_record  # Unused.
+        return None
+
+      def merge_sample_states(self, sample_state_1, sample_state_2):
+        del sample_state_1, sample_state_2  # Unused.
+        return None
+
+      def get_noised_result(self, sample_state, global_state):
+        del sample_state, global_state  # Unused.
+        return None
 
     non_sum_query = NonSumDPQuery()
 
@@ -138,6 +154,23 @@ class NestedQueryTest(tf.test.TestCase, parameterized.TestCase):
       def __init__(self, metric_val):
         self._metric_val = metric_val
 
+      def initial_sample_state(self, template=None):
+        del template  # Unused.
+        return None
+
+      def accumulate_preprocessed_record(self, sample_state,
+                                         preprocessed_record):
+        del sample_state, preprocessed_record  # Unused.
+        return None
+
+      def merge_sample_states(self, sample_state_1, sample_state_2):
+        del sample_state_1, sample_state_2  # Unused.
+        return None
+
+      def get_noised_result(self, sample_state, global_state):
+        del sample_state, global_state  # Unused.
+        return None
+
       def derive_metrics(self, global_state):
         return collections.OrderedDict(metric=self._metric_val)