resolving review comments

Signed-off-by: akhilsaivenkata <[email protected]>

sdk/python/v1beta1/kubeflow/katib/api/katib_client.py

@@ -283,6 +283,30 @@ def tune(
         if max_failed_trial_count is not None:
             experiment.spec.max_failed_trial_count = max_failed_trial_count
 
+        # Iterate over input parameters.
+        input_params = {}
+        experiment_params = []
+        trial_params = []
+        base_image = constants.BASE_IMAGE_TENSORFLOW,
+
+        for p_name, p_value in parameters.items():
+            # If input parameter value is Katib Experiment parameter sample.
+            if isinstance(p_value, models.V1beta1ParameterSpec):
+                # Wrap value for the function input.
+                input_params[p_name] = f"${{trialParameters.{p_name}}}"
+
+                # Add value to the Katib Experiment parameters.
+                p_value.name = p_name
+                experiment_params.append(p_value)
+
+                # Add value to the Katib Experiment's Trial parameters.
+                trial_params.append(
+                    models.V1beta1TrialParameterSpec(name=p_name, reference=p_name)
+                )
+            else:
+                # Otherwise, add value to the function input.
+                input_params[p_name] = p_value
+
         # Handle different types of objective input
         if callable(objective):
             # Validate objective function.
@@ -295,29 +319,6 @@ def tune(
             # (e.g. in another function). We need to dedent the function code.
             objective_code = textwrap.dedent(objective_code)
 
-            # Iterate over input parameters.
-            input_params = {}
-            experiment_params = []
-            trial_params = []
-            base_image = constants.BASE_IMAGE_TENSORFLOW,
-            for p_name, p_value in parameters.items():
-                # If input parameter value is Katib Experiment parameter sample.
-                if isinstance(p_value, models.V1beta1ParameterSpec):
-                    # Wrap value for the function input.
-                    input_params[p_name] = f"${{trialParameters.{p_name}}}"
-
-                    # Add value to the Katib Experiment parameters.
-                    p_value.name = p_name
-                    experiment_params.append(p_value)
-
-                    # Add value to the Katib Experiment's Trial parameters.
-                    trial_params.append(
-                        models.V1beta1TrialParameterSpec(name=p_name, reference=p_name)
-                    )
-                else:
-                    # Otherwise, add value to the function input.
-                    input_params[p_name] = p_value
-
             # Wrap objective function to execute it from the file. For example
             # def objective(parameters):
             #     print(f'Parameters are {parameters}')
@@ -407,12 +408,12 @@ def tune(
         trial_template = models.V1beta1TrialTemplate(
             primary_container_name=constants.DEFAULT_PRIMARY_CONTAINER_NAME,
             retain=retain_trials,
-            trial_parameters=trial_params if callable(objective) else [],
+            trial_parameters=trial_params,
             trial_spec=trial_spec,
         )
 
         # Add parameters to the Katib Experiment.
-        experiment.spec.parameters = experiment_params if callable(objective) else []
+        experiment.spec.parameters = experiment_params
 
         # Add Trial template to the Katib Experiment.
         experiment.spec.trial_template = trial_template