update

Author: jtsodapop

labelbox/schema/data_row_metadata.py

@@ -46,6 +46,7 @@ def id(self):
 
 
 class _CamelCaseMixin(BaseModel):
+
     class Config:
         allow_population_by_field_name = True
         alias_generator = camel_case
@@ -111,6 +112,7 @@ class DataRowMetadataOntology:
     >>> mdo = client.get_data_row_metadata_ontology()
 
     """
+
     def __init__(self, client):
 
         self._client = client
@@ -130,8 +132,8 @@ def _build_ontology(self):
         ]
         self.reserved_by_id = self._make_id_index(self.reserved_fields)
         self.reserved_by_name: Dict[
-            str, DataRowMetadataSchema] = self._make_name_index(
-                self.reserved_fields)
+            str,
+            DataRowMetadataSchema] = self._make_name_index(self.reserved_fields)
 
         # custom fields
         self.custom_fields: List[DataRowMetadataSchema] = [
@@ -207,8 +209,9 @@ def _parse_ontology(raw_ontology) -> List[DataRowMetadataSchema]:
         return fields
 
     def parse_metadata(
-        self, unparsed: List[Dict[str, List[Union[str, Dict]]]]
-    ) -> List[DataRowMetadata]:
+        self, unparsed: List[Dict[str,
+                                  List[Union[str,
+                                             Dict]]]]) -> List[DataRowMetadata]:
         """ Parse metadata responses
 
         >>> mdo.parse_metadata([metdata])
@@ -301,8 +304,8 @@ def _batch_upsert(
                     data_row_id=m.data_row_id,
                     fields=list(
                         chain.from_iterable(
-                            self._parse_upsert(m)
-                            for m in m.fields))).dict(by_alias=True))
+                            self._parse_upsert(m) for m in m.fields))).dict(
+                                by_alias=True))
 
         res = _batch_operations(_batch_upsert, items, self._batch_size)
         return res
@@ -468,12 +471,11 @@ def _batch_operations(
 
 
 def _validate_parse_embedding(
-    field: DataRowMetadataField
+        field: DataRowMetadataField
 ) -> List[Dict[str, Union[SchemaId, Embedding]]]:
 
     if isinstance(field.value, list):
-        if not (Embedding.min_items <= len(field.value) <=
-                Embedding.max_items):
+        if not (Embedding.min_items <= len(field.value) <= Embedding.max_items):
             raise ValueError(
                 "Embedding length invalid. "
                 "Must have length within the interval "

labelbox/schema/model_run.py

@@ -123,8 +123,8 @@ def add_predictions(
                 return MEAPredictionImport.create_from_file(
                     path=str(predictions), **kwargs)
             else:
-                return MEAPredictionImport.create_from_url(
-                    url=str(predictions), **kwargs)
+                return MEAPredictionImport.create_from_url(url=str(predictions),
+                                                           **kwargs)
         elif isinstance(predictions, Iterable):
             return MEAPredictionImport.create_from_objects(
                 predictions=predictions, **kwargs)