class_docstr: Handles decoder-based text architectures.
class_docstr_tok: ["Handles","decoder-based","text","architectures","."]
code: def __init__(
      self,
      config: "PretrainedConfig",
      task: str = "feature-extraction",
      int_dtype: str = "int64",
      float_dtype: str = "fp32",
      use_past: bool = False,
      use_past_in_inputs: bool = False,
      preprocessors: Optional[List[Any]] = None,
      legacy: bool = False,
  ):
      super().__init__(
          config=config,
          task=task,
          int_dtype=int_dtype,
          float_dtype=float_dtype,
          use_past=use_past,
          use_past_in_inputs=use_past_in_inputs,
          preprocessors=preprocessors,
          legacy=legacy,
      )
  
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 to create a new TextDecoderOnnxConfig object from inherited base classes: OnnxConfigWithPast with example: obj = TextDecoderOnnxConfig(config, task, int_dtype, float_dtype, use_past, use_past_in_inputs, preprocessors, legacy)