Merge pull request apache#16562 from [BEAM-13051][D] Enable pylint wa…

…rnings (no-name-in-module/no-value-for-parameter)

* [BEAM-13051] Pylint no-name-in-module and no-value-for-parameter warnings enabled

* [BEAM-13051] Fixed no-value-for-parameter warning for missing default values

* [BEAM-13051] Fixed parameters warnings

sdks/python/apache_beam/io/avroio_test.py

@@ -95,7 +95,13 @@ def tearDown(self):
         os.remove(path)
     self._temp_files = []
 
-  def _write_data(self, directory, prefix, codec, count, sync_interval):
+  def _write_data(
+      self,
+      directory=None,
+      prefix=None,
+      codec=None,
+      count=None,
+      sync_interval=None):
     raise NotImplementedError
 
   def _write_pattern(self, num_files, return_filenames=False):

sdks/python/apache_beam/io/filesystem.py

@@ -257,8 +257,7 @@ def _read_from_internal_buffer(self, read_fn):
     self._read_buffer.seek(0, os.SEEK_END)  # Allow future writes.
     return result
 
-  def read(self, num_bytes):
-    # type: (int) -> bytes
+  def read(self, num_bytes: Optional[int] = None) -> bytes:
     if not self._decompressor:
       raise ValueError('decompressor not initialized')
 

sdks/python/apache_beam/testing/load_tests/load_test_metrics_utils.py

@@ -216,7 +216,7 @@ def __init__(
           'InfluxDB')
     self.filters = filters
 
-  def publish_metrics(self, result, extra_metrics: dict):
+  def publish_metrics(self, result, extra_metrics: Optional[dict] = None):
     metric_id = uuid.uuid4().hex
     metrics = result.metrics().query(self.filters)
 
@@ -227,13 +227,16 @@ def publish_metrics(self, result, extra_metrics: dict):
     # a list of dictionaries matching the schema.
     insert_dicts = self._prepare_all_metrics(metrics, metric_id)
 
-    insert_dicts += self._prepare_extra_metrics(extra_metrics, metric_id)
+    insert_dicts += self._prepare_extra_metrics(metric_id, extra_metrics)
     if len(insert_dicts) > 0:
       for publisher in self.publishers:
         publisher.publish(insert_dicts)
 
-  def _prepare_extra_metrics(self, extra_metrics: dict, metric_id: str):
+  def _prepare_extra_metrics(
+      self, metric_id: str, extra_metrics: Optional[dict] = None):
     ts = time.time()
+    if not extra_metrics:
+      extra_metrics = {}
     return [
         Metric(ts, metric_id, v, label=k).as_dict() for k,
         v in extra_metrics.items()