feat(automl): add support for feature importance (#9816)

Previously feature importance is added with a params parameter, the
user has to set params = {"feature_importance": "true"}. This PR
simplifies the logic, the user just have to pass feature_importance =
True.

automl/google/cloud/automl_v1beta1/tables/tables_client.py

@@ -2596,7 +2596,7 @@ def predict(
         model=None,
         model_name=None,
         model_display_name=None,
-        params=None,
+        feature_importance=False,
         project=None,
         region=None,
         **kwargs
@@ -2643,9 +2643,9 @@ def predict(
                 The `model` instance you want to predict with . This must be
                 supplied if `model_display_name` or `model_name` are not
                 supplied.
-            params (dict[str, str]):
-                `feature_importance` can be set as True to enable local
-                explainability. The default is false.
+            feature_importance (bool):
+                True if enable feature importance explainability. The default is
+                False.
 
         Returns:
             A :class:`~google.cloud.automl_v1beta1.types.PredictResponse`
@@ -2687,6 +2687,10 @@ def predict(
 
         request = {"row": {"values": values}}
 
+        params = None
+        if feature_importance:
+            params = {"feature_importance": "true"}
+
         return self.prediction_client.predict(model.name, request, params, **kwargs)
 
     def batch_predict(

automl/tests/unit/gapic/v1beta1/test_tables_client_v1beta1.py

@@ -1137,6 +1137,25 @@ def test_predict_from_dict(self):
             None,
         )
 
+    def test_predict_from_dict_with_feature_importance(self):
+        data_type = mock.Mock(type_code=data_types_pb2.CATEGORY)
+        column_spec_a = mock.Mock(display_name="a", data_type=data_type)
+        column_spec_b = mock.Mock(display_name="b", data_type=data_type)
+        model_metadata = mock.Mock(
+            input_feature_column_specs=[column_spec_a, column_spec_b]
+        )
+        model = mock.Mock()
+        model.configure_mock(tables_model_metadata=model_metadata, name="my_model")
+        client = self.tables_client({"get_model.return_value": model}, {})
+        client.predict(
+            {"a": "1", "b": "2"}, model_name="my_model", feature_importance=True
+        )
+        client.prediction_client.predict.assert_called_with(
+            "my_model",
+            {"row": {"values": [{"string_value": "1"}, {"string_value": "2"}]}},
+            {"feature_importance": "true"},
+        )
+
     def test_predict_from_dict_missing(self):
         data_type = mock.Mock(type_code=data_types_pb2.CATEGORY)
         column_spec_a = mock.Mock(display_name="a", data_type=data_type)