diff --git a/algorithmic_efficiency/workloads/librispeech_conformer/librispeech_jax/workload.py b/algorithmic_efficiency/workloads/librispeech_conformer/librispeech_jax/workload.py
index 3caf151ab..e362f973b 100644
--- a/algorithmic_efficiency/workloads/librispeech_conformer/librispeech_jax/workload.py
+++ b/algorithmic_efficiency/workloads/librispeech_conformer/librispeech_jax/workload.py
@@ -113,7 +113,6 @@ def model_fn(
     variables = {'params': params, **model_state}
     inputs, input_paddings = augmented_and_preprocessed_input_batch['inputs']
     is_train_mode = mode == spec.ForwardPassMode.TRAIN
-    print(type(use_running_average_bn))
     if update_batch_norm or is_train_mode:
       (logits, logit_paddings), new_model_state = self._model.apply(
           variables,
diff --git a/algorithmic_efficiency/workloads/librispeech_deepspeech/librispeech_jax/workload.py b/algorithmic_efficiency/workloads/librispeech_deepspeech/librispeech_jax/workload.py
index 8473fac0f..c81b1b0b4 100644
--- a/algorithmic_efficiency/workloads/librispeech_deepspeech/librispeech_jax/workload.py
+++ b/algorithmic_efficiency/workloads/librispeech_deepspeech/librispeech_jax/workload.py
@@ -55,6 +55,37 @@ def init_model_fn(
     model_state = jax_utils.replicate(model_state)
     params = jax_utils.replicate(params)
     return params, model_state
+  
+   def model_fn(
+      self,
+      params: spec.ParameterContainer,
+      augmented_and_preprocessed_input_batch: Dict[str, spec.Tensor],
+      model_state: spec.ModelAuxiliaryState,
+      mode: spec.ForwardPassMode,
+      rng: spec.RandomState,
+      update_batch_norm: bool,
+      use_running_average_bn: Optional[bool] = None
+  ) -> Tuple[spec.Tensor, spec.ModelAuxiliaryState]:
+    variables = {'params': params, **model_state}
+    inputs, input_paddings = augmented_and_preprocessed_input_batch['inputs']
+    is_train_mode = mode == spec.ForwardPassMode.TRAIN
+    if update_batch_norm or is_train_mode:
+      (logits, logit_paddings), new_model_state = self._model.apply(
+          variables,
+          inputs,
+          input_paddings,
+          train=True,
+          rngs={'dropout' : rng},
+          mutable=['batch_stats'])
+      return (logits, logit_paddings), new_model_state
+    else:
+      logits, logit_paddings = self._model.apply(
+          variables,
+          inputs,
+          input_paddings,
+          train=False,
+          mutable=False)
+      return (logits, logit_paddings), model_state
 
   def is_output_params(self, param_key: spec.ParameterKey) -> bool:
     return param_key == 'Dense_0'
diff --git a/tests/reference_algorithm_tests.py b/tests/reference_algorithm_tests.py
index 74c06e180..5e563d2f9 100644
--- a/tests/reference_algorithm_tests.py
+++ b/tests/reference_algorithm_tests.py
@@ -408,6 +408,7 @@ def _test_submission(workload_name,
       workload_path=workload_metadata['workload_path'],
       workload_class_name=workload_metadata['workload_class_name'],
       return_class=True)
+  print(f'Workload class for {workload_name} is {workload_class}')
 
   submission_module_path = workloads.convert_filepath_to_module(submission_path)
   submission_module = importlib.import_module(submission_module_path)