code: def __init__(
      self,
      model: onnxruntime.InferenceSession,
      config: "PretrainedConfig",
      use_io_binding: Optional[bool] = None,
      model_save_dir: Optional[
          Union[str, Path, TemporaryDirectory]
      ] = None,
      preprocessors: Optional[List] = None,
      generation_config: Optional[GenerationConfig] = None,
      use_cache: Optional[bool] = None,
      **kwargs,
  ):
      super().__init__(
          model=model,
          config=config,
          use_io_binding=use_io_binding,
          model_save_dir=model_save_dir,
          preprocessors=preprocessors,
          generation_config=generation_config,
          use_cache=use_cache,
          **kwargs,
      )
      self.num_key_value_heads = (
          config.num_kv_heads
          if (config.new_decoder_architecture or not config.multi_query)
          else 1
      )
      self.use_alibi = config.alibi
  
imports: import logging
  from pathlib import Path
  from tempfile import TemporaryDirectory
  from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
  import numpy
  import onnx
  import torch
  from onnx.tools import update_model_dims
  from transformers import AutoModelForCausalLM, GenerationConfig
  from transformers.file_utils import add_end_docstrings, add_start_docstrings_to_model_forward
  from transformers.modeling_outputs import CausalLMOutputWithPast
  import onnxruntime
  from ..exporters.onnx import MODEL_TYPES_REQUIRING_POSITION_IDS, main_export
  from ..onnx.utils import check_model_uses_external_data
  from ..utils import NormalizedConfigManager, check_if_transformers_greater
  from ..utils.modeling_utils import MODEL_TO_PATCH_FOR_PAST
  from ..utils.save_utils import maybe_save_preprocessors
  from .constants import DECODER_MERGED_ONNX_FILE_PATTERN, DECODER_ONNX_FILE_PATTERN, DECODER_WITH_PAST_ONNX_FILE_PATTERN
  from .modeling_ort import ONNX_MODEL_END_DOCSTRING, ORTModel
  from .models.bloom import bloom_convert_to_bloom_cache, bloom_convert_to_standard_cache
  from .utils import ONNX_DECODER_NAME, ONNX_DECODER_WITH_PAST_NAME, ONNX_WEIGHTS_NAME
label_desc: Use image node_id 1 to create a new ORTFalconForCausalLM object from inherited base classes: ORTModelForCausalLM with example: obj = ORTFalconForCausalLM(model, config, use_io_binding, model_save_dir, preprocessors, generation_config, use_cache)