class_docstr: Handles decoder-based text architectures.
class_docstr_tok: ["Handles","decoder-based","text","architectures","."]
code: def inputs(self) -> Dict[str, Dict[int, str]]:
      if self.use_past_in_inputs:
          common_inputs = {
              "input_ids": {0: "batch_size", 1: "sequence_length"}
          }
          self.add_past_key_values(common_inputs, direction="inputs")
          common_inputs["attention_mask"] = {
              0: "batch_size",
              1: "past_sequence_length + 1",
          }
      else:
          common_inputs = {
              "input_ids": {0: "batch_size", 1: "sequence_length"},
              "attention_mask": {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 2 for calling the TextDecoderOnnxConfig obj's underlying member method code with example usage: obj.inputs() and returns: common_inputs