class_docstr: Handles encoder-decoder-based text architectures.
class_docstr_tok: ["Handles","encoder-decoder-based","text","architectures","."]
code: def torch_to_onnx_input_map(self) -> Dict[str, str]:
      if self._behavior is ConfigBehavior.DECODER:
          return {
              "decoder_input_ids": "input_ids",
              "encoder_outputs": "encoder_hidden_states",
              "attention_mask": "encoder_attention_mask",
          }
      return {}
  
imports: from collections import OrderedDict
  from pathlib import Path
  from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Union
  from transformers.utils import is_tf_available
  from ...onnx import merge_decoders
  from ...utils import DummyAudioInputGenerator, DummyBboxInputGenerator, DummyInputGenerator, DummyPastKeyValuesGenerator, DummySeq2SeqDecoderTextInputGenerator, DummySeq2SeqPastKeyValuesGenerator, DummyTextInputGenerator, DummyVisionInputGenerator, is_diffusers_available, logging
  from .base import ConfigBehavior, OnnxConfig, OnnxConfigWithPast, OnnxSeq2SeqConfigWithPast
  from .constants import ONNX_DECODER_MERGED_NAME, ONNX_DECODER_NAME, ONNX_DECODER_WITH_PAST_NAME
  from .model_patcher import DecoderModelPatcher
label_desc: Use image node_id 1 for calling the TextSeq2SeqOnnxConfig obj's underlying member method code with example usage: obj.torch_to_onnx_input_map() and returns: dict, dict