class_docstr: Handles decoder-based text architectures.
class_docstr_tok: ["Handles","decoder-based","text","architectures","."]
code: def outputs(self) -> Dict[str, Dict[int, str]]:
      if self.is_merged is False:
          common_outputs = super().outputs
      else:
          # in the merged case, we need to allow the `sequence_length` to be variable, as it is not 1
          # during the first pass without past key values
          common_outputs = OrderedDict(
              {"logits": {0: "batch_size", 1: "sequence_length"}}
          )
          self.add_past_key_values(common_outputs, direction="outputs")
      return common_outputs
  
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 3 for calling the TextDecoderOnnxConfig obj's underlying member method code with example usage: obj.outputs() and returns: common_outputs