Apply Black 20.8b1 formatting

eland/ml/imported_ml_model.py

@@ -218,7 +218,8 @@ class ImportedMLModel(MLModel):
             body["definition"] = serializer.serialize_model()
 
         self._client.ml.put_trained_model(
-            model_id=self._model_id, body=body,
+            model_id=self._model_id,
+            body=body,
         )
 
     def predict(self, X: Union[List[float], List[List[float]]]) -> np.ndarray:

eland/ml/transformers/lightgbm.py

@@ -93,7 +93,8 @@ class LGBMForestTransformer(ModelTransformer):
         self, tree_id: int, node_id: int, tree_node_json_obj: Dict[str, Any]
     ) -> TreeNode:
         return TreeNode(
-            node_idx=node_id, leaf_value=[float(tree_node_json_obj["leaf_value"])],
+            node_idx=node_id,
+            leaf_value=[float(tree_node_json_obj["leaf_value"])],
         )
 
     def build_tree(self, tree_id: int, tree_json_obj: Dict[str, Any]) -> Tree:

eland/query.py

@@ -137,7 +137,12 @@ class Query:
         self._aggs[name] = agg
 
     def hist_aggs(
-        self, name: str, field: str, min_value: Any, max_value: Any, num_bins: int,
+        self,
+        name: str,
+        field: str,
+        min_value: Any,
+        max_value: Any,
+        num_bins: int,
     ) -> None:
         """
         Add histogram agg e.g.

eland/tests/__init__.py

@@ -30,7 +30,8 @@ ELASTICSEARCH_HOST = os.environ.get("ELASTICSEARCH_HOST") or "localhost"
 TEST_SUITE = os.environ.get("TEST_SUITE", "xpack")
 if TEST_SUITE == "xpack":
     ES_TEST_CLIENT = Elasticsearch(
-        ELASTICSEARCH_HOST, http_auth=("elastic", "changeme"),
+        ELASTICSEARCH_HOST,
+        http_auth=("elastic", "changeme"),
     )
 else:
     ES_TEST_CLIENT = Elasticsearch(ELASTICSEARCH_HOST)