class_docstr: Decoder model with a language modeling head on top for ONNX Runtime inference.
class_docstr_tok: ["Decoder","model","with","a","language","modeling","head","on","top","for","ONNX","Runtime","inference","."]
code: def get_outputs_not_to_bind(self, use_merged_cache: bool) -> Set[str]:
      result = {
          output_name
          for output_name in self.output_names
          if (
              not output_name.startswith("present")
              and output_name not in {"loss", "logits"}
          )
      }
      if use_merged_cache is True:
          # When using a merged decoder and the use cache branch, we output 0-dim tensors that IO Binding do not support.
          # Therefore, we do not bind them.
          result = result.union(
              self.past_key_values_cross_attention_output_names
          )
      return result
  
imports: from abc import abstractmethod
  from typing import TYPE_CHECKING, Dict, Optional, Set, Tuple, Union
  import numpy
  import torch
  from transformers.modeling_outputs import BaseModelOutput, Seq2SeqLMOutput
  from onnxruntime import InferenceSession
  from ..utils import NormalizedConfigManager
  from ..utils.logging import warn_once
  from .utils import get_ordered_input_names, logging
label_desc: Use image node_id 3 for calling the ORTDecoderForSeq2Seq obj's underlying member method code with example usage: obj.get_outputs_not_to_bind(use_merged_cache) and returns: result