code: def inputs(self) -> Dict[str, Dict[int, str]]:
      common_inputs = super().inputs
  
      # Decoders based on GPT2 require a position_ids input to avoid
      # generating wrong position_ids in the model itself:
      # https://github.com/huggingface/transformers/blob/v4.33.1/src/transformers/models/gpt2/modeling_gpt2.py#L802
      if not self.legacy and self.task in [
          "text-generation",
          "feature-extraction",
      ]:
          common_inputs["position_ids"] = {
              0: "batch_size",
              1: "sequence_length",
          }
  
      return common_inputs
  
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 TextDecoderWithPositionIdsOnnxConfig obj's underlying member method code with example usage: obj.inputs() and returns: common_inputs