clean comments

optimum/exporters/neuron/base.py

@@ -363,10 +363,6 @@ def forward(self, *input):
                 if isinstance(outputs, dict):
                     if eligible_outputs is not None:
                         outputs = {name: outputs[name] for name in outputs.keys() & eligible_outputs}
-                    # if output_hidden_states:
-                    #     outputs["hidden_states"] = list(
-                    #         outputs["hidden_states"]
-                    #     )  # flatten `hidden_states` which is a tuple of tensors
 
                 if isinstance(outputs, tuple) and eligible_outputs is not None:
                     if not all(isinstance(x, int) for x in eligible_outputs):

optimum/neuron/modeling_diffusion.py

@@ -855,15 +855,9 @@ def forward(
         input_ids: torch.Tensor,
         *args,
         **kwargs,
-        # attention_mask: Optional[torch.Tensor] = None,
-        # output_hidden_states: bool = False,
-        # return_dict: bool = False,
     ):
         input_ids = input_ids.to(torch.long)  # dummy generator uses long int for tracing
 
-        # if output_hidden_states is not self.neuron_config.output_hidden_states:
-        #     logger.warning(f"The model has been traced with output_hidden_states={self.config.output_hidden_states}, the outputs of the model might not be what you expect.")
-
         inputs = (input_ids,)
         outputs = self.model(*inputs)