code: def forward(
      self,
      input_ids=None,
      attention_mask=None,
      head_mask=None,
      inputs_embeds=None,
      labels=None,
      output_attentions=None,
      output_hidden_states=None,
      return_dict=None,
  ):
      if self.training or labels is not None:
          raise ValueError(WARN_MSG_FOR_TRAINING_ATTEMPT)
  
      return_dict = (
          return_dict
          if return_dict is not None
          else self.config.use_return_dict
      )
      if return_dict:
          raise ValueError(WARN_MSG_FOR_DICT_RETURN)
  
      dlbrt_output = self.distilbert(
          input_ids=input_ids,
          attention_mask=attention_mask,
          head_mask=head_mask,
          inputs_embeds=inputs_embeds,
          output_attentions=output_attentions,
          output_hidden_states=output_hidden_states,
          return_dict=False,
      )
      hidden_states = dlbrt_output[0]  # (bs, dim, 1, seq_len)
      prediction_logits = self.vocab_transform(
          hidden_states
      )  # (bs, dim, 1, seq_len)
      prediction_logits = self.activation(
          prediction_logits
      )  # (bs, dim, 1, seq_len)
      prediction_logits = self.vocab_layer_norm(
          prediction_logits
      )  # (bs, dim, 1, seq_len)
      prediction_logits = self.vocab_projector(
          prediction_logits
      )  # (bs, dim, 1, seq_len)
      prediction_logits = prediction_logits.squeeze(-1).squeeze(
          -1
      )  # (bs, dim)
  
      output = (prediction_logits,) + dlbrt_output[1:]
      mlm_loss = None
  
      return ((mlm_loss,) + output) if mlm_loss is not None else output
  
imports: from ane_transformers.reference.layer_norm import LayerNormANE
  import torch
  import torch.nn
  from transformers.models.distilbert import modeling_distilbert
label_desc: Use image node_id 2 for calling the DistilBertForMaskedLM obj's underlying member method code with example usage: obj.forward(input_ids, attention_mask, head_mask, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict) and returns: unknown