[*.py] Rename "Arguments:" to "Args:"

Author: SamuelMarks

src/python/tensorflow_cloud/tuner/tuner.py

@@ -301,7 +301,7 @@ def end_trial(self, trial_id: Text, status: Text = "COMPLETED"):
     def get_best_trials(self, num_trials: int = 1) -> List[trial_module.Trial]:
         """Returns the trials with the best objective values found so far.
 
-        Arguments:
+        Args:
             num_trials: positive int, number of trials to return.
         Returns:
             List of KerasTuner Trials.
@@ -501,7 +501,7 @@ def run_trial(self, trial, *fit_args, **fit_kwargs):
 
         This method is called during `search` to evaluate a set of
         hyperparameters using AI Platform training.
-        Arguments:
+        Args:
             trial: A `Trial` instance that contains the information
               needed to run this trial. `Hyperparameters` can be accessed
               via `trial.hyperparameters`.
@@ -637,7 +637,7 @@ def run_trial(self, trial, *fit_args, **fit_kwargs):
     def _get_job_spec_from_config(self, job_id: Text) -> Dict[Text, Any]:
         """Creates a request dictionary for the CAIP training service.
 
-        Arguments:
+        Args:
             job_id: Job name that will be used for AIP training
         Returns:
             An AI Platform Training job spec.
@@ -681,7 +681,7 @@ def _get_remote_training_metrics(
         All complete epochs metrics (including the last epoch if applicable) are
         returned as training_metrics.
 
-        Arguments:
+        Args:
             log_reader: An instance of tensorboard DirectoryWatcher that is
                 pointing to the tensorboard logs directory.
             partial_epoch_metrics: Any incomplete epoch metrics from previous
@@ -742,7 +742,7 @@ def _add_logging(self, callbacks, trial):
         TensorBoard callback to pass back the epoch related metrics from
         remote execution.
 
-        Arguments:
+        Args:
             callbacks: List of callbacks passed in to the search function.
             trial: A `Trial` instance.
         Raises:

src/python/tensorflow_cloud/tuner/utils.py

@@ -52,7 +52,7 @@ def make_study_config(
     hyperparams: hp_module.HyperParameters) -> Dict[Text, Any]:
     """Generates Optimizer study_config from kerastuner configurations.
 
-    Arguments:
+    Args:
         objective: String or `oracle_module.Objective`. If a string,
             the direction of the optimization (min or max) will be inferred.
         hyperparams: HyperParameters class instance. Can be used to override (or
@@ -383,7 +383,7 @@ def format_objective(
     direction: Text = None) -> List[oracle_module.Objective]:
     """Formats objective to a list of oracle_module.Objective.
 
-    Arguments:
+    Args:
         objective: If a string, the direction of the optimization (min or max)
             will be inferred.
         direction: Optional. e.g. 'min' or 'max'.
@@ -456,7 +456,7 @@ def _get_scale_type(sampling):
 def get_trial_id(optimizer_trial: Dict[Text, Any]) -> Text:
     r"""Gets trial_id from a CAIP Optimizer Trial.
 
-    Arguments:
+    Args:
         optimizer_trial: A CAIP Optimizer Trial instance.
 
     Returns:
@@ -472,7 +472,7 @@ def convert_optimizer_trial_to_dict(
 ) -> Dict[Text, Any]:
     """Converts Optimizer Trial parameters into a Python dict.
 
-    Arguments:
+    Args:
         optimizer_trial: A CAIP Optimizer Trial instance.
 
     Returns:
@@ -496,7 +496,7 @@ def convert_optimizer_trial_to_hps(
 ) -> hp_module.HyperParameters:
     """Converts Optimizer Trial parameters into KerasTuner HyperParameters.
 
-    Arguments:
+    Args:
         hps: Sample KerasTuner HyperParameters object for config initialization
         optimizer_trial: A CAIP Optimizer Trial instance.
 
@@ -515,7 +515,7 @@ def convert_completed_optimizer_trial_to_keras_trial(
 ) -> trial_module.Trial:
     """Converts completed Optimizer Trial into KerasTuner Trial.
 
-    Arguments:
+    Args:
         optimizer_trial: A CAIP Optimizer Trial Instance.
         hyperparameter_space: Mandatory and must include definitions for all
             hyperparameters used during the search.