class_docstr: Creation of a class to combine the decoder and the lm head 
class_docstr_tok: ["Creation","of","a","class","to","combine","the","decoder","and","the","lm","head"]
code: def forward(self, *inputs):
      input_ids, attention_mask, encoder_hidden_states = inputs[:3]
  
      list_pkv = inputs[3:]
      past_key_values = tuple(
          list_pkv[i : i + 4] for i in range(0, len(list_pkv), 4)
      )
  
      decoder_output = self.decoder(
          input_ids=input_ids,  # decoder_input_ids
          encoder_attention_mask=attention_mask,
          encoder_hidden_states=encoder_hidden_states,
          past_key_values=past_key_values,
      )
  
      lm_head_out = self.lm_head(
          decoder_output[0] * (self.config.d_model**-0.5)
      )
  
      return lm_head_out, decoder_output[1]
  
file_path: fastT5/fastT5/onnx_models_structure.py
imports: import torch
label_desc: Use image node_id 2 for calling the DecoderWithLMhead obj's underlying member method code with example usage: obj.forward() and returns: lm_head_out,  decoder_output