class_docstr: Decoder model with a language modeling head on top.
  Arguments:
      model (`transformers.PreTrainedModel`):
          The model from which to extract the decoder and the language modeling head.
code: def __init__(self, model: PreTrainedModel):
      super().__init__(model.config)
      self.config = model.config
      self.decoder = model.get_decoder()
      self.lm_head = model.get_output_embeddings()
      self.final_logits_bias = getattr(model, "final_logits_bias", None)
  
file_path: optimum/optimum/onnx/modeling_seq2seq.py
imports: from typing import Optional, Tuple
  import torch
  from torch.nn import CrossEntropyLoss
  from transformers import PreTrainedModel
  from transformers.file_utils import add_start_docstrings_to_model_forward
label_desc: Use image node_id 1 to create a new _DecoderWithLMhead object from inherited base classes: PreTrainedModel with example: obj = _DecoderWithLMhead(model)