fix style issue

Author: BryanCutler

tensorflow_io/arrow/python/ops/arrow_dataset_ops.py

@@ -139,13 +139,16 @@ def __init__(self,
       writer.write_batch(batch)
     writer.close()
     self._serialized_batches = ops.convert_to_tensor(
-        buf.getvalue(), dtype=dtypes.string, name="serialized_batches")
+        buf.getvalue(),
+        dtype=dtypes.string,
+        name="serialized_batches")
 
   def _as_variant_tensor(self):
-    return arrow_ops.arrow_dataset(self._serialized_batches,
-                                   self._columns,
-                                   nest.flatten(self.output_types),
-                                   nest.flatten(self.output_shapes))
+    return arrow_ops.arrow_dataset(
+        self._serialized_batches,
+        self._columns,
+        nest.flatten(self.output_types),
+        nest.flatten(self.output_shapes))
 
   @classmethod
   def from_pandas(cls, df, columns=None, preserve_index=True):
@@ -193,14 +196,16 @@ def __init__(self,
                                               output_types,
                                               output_shapes)
     self._filenames = ops.convert_to_tensor(
-        filenames, dtype=dtypes.string, name="filenames")
+        filenames,
+        dtype=dtypes.string,
+        name="filenames")
 
   def _as_variant_tensor(self):
-    return arrow_ops.\
-        arrow_feather_dataset(self._filenames,
-                              self._columns,
-                              nest.flatten(self.output_types),
-                              nest.flatten(self.output_shapes))
+    return arrow_ops.arrow_feather_dataset(
+        self._filenames,
+        self._columns,
+        nest.flatten(self.output_types),
+        nest.flatten(self.output_shapes))
 
   @classmethod
   def from_schema(cls, filenames, schema, columns=None):
@@ -244,14 +249,15 @@ def __init__(self,
                                              output_types,
                                              output_shapes)
     self._host = ops.convert_to_tensor(
-        host, dtype=dtypes.string, name="host")
+        host,
+        dtype=dtypes.string,
+        name="host")
 
   def _as_variant_tensor(self):
-    return arrow_ops.\
-        arrow_stream_dataset(self._host,
-                             self._columns,
-                             nest.flatten(self.output_types),
-                             nest.flatten(self.output_shapes))
+    return arrow_ops.arrow_stream_dataset(
+        self._host, self._columns,
+        nest.flatten(self.output_types),
+        nest.flatten(self.output_shapes))
 
   @classmethod
   def from_schema(cls, host, schema, columns=None):