code: def test_llama_decoder_with_local_backend(
      self, test_local_backend: bool, test_backward: bool
  ):
      from transformers import LlamaConfig  # noqa: F811
      from transformers.models.llama.modeling_llama import (  # noqa: F811
          LlamaDecoderLayer,
      )
  
      hidden_size = 16
  
      config = LlamaConfig(
          num_hidden_layers=1,
          vocab_size=1024,
          hidden_size=hidden_size,
          intermediate_size=16,
          max_position_embeddings=256,
          num_attention_heads=2,
          hidden_dropout_prob=0.0,
          attention_dropout_prob=0.0,
      )
  
      class LlamaDecoderWrapper(torch.nn.Module):
          def __init__(self, config):
              super().__init__()
              try:
                  # New version of LlamaDecoderLayer has layer_idx argument.
                  self.decoder = LlamaDecoderLayer(config, layer_idx=0)
              except TypeError:
                  # Fall back to old version of LlamaDecoderLayer.
                  self.decoder = LlamaDecoderLayer(config)
  
          def forward(
              self, hidden_states, attention_mask, position_ids
          ):
              (decoder_output,) = self.decoder(
                  hidden_states, attention_mask, position_ids
              )
              return decoder_output
  
      def generate_example_inputs(
          batch: int, seq: int, hidden_size: int
      ):
          # shape: batch x seq x hidden_size
          hidden_state = torch.randn(batch, seq, hidden_size)
          # [0.0000e+00, ..., 0.0000e+00, -3.4028e+38, ...]
          # shape: batch x 1 x seq x seq
          attention_mask = torch.zeros(
              batch, 1, seq, seq, dtype=torch.float
          )
          position_ids = torch.arange(0, seq, dtype=torch.int64)
          position_ids = position_ids.unsqueeze(0).view(-1, seq)
          return hidden_state, attention_mask, position_ids
  
      # Reason for using multiple example argument groups:
      #  Export model to ONNX with one example argument group
      #  and test it with other example argument groups.
      example_args_collection = (
          generate_example_inputs(2, 8, hidden_size),
          generate_example_inputs(4, 7, hidden_size),
          generate_example_inputs(9, 15, hidden_size),
      )
  
      if test_local_backend:
          local_aot_ort, local_ort = make_aot_ort(dynamic=True)
      else:
          local_aot_ort, local_ort = "onnxrt", None
  
      model = LlamaDecoderWrapper(config).eval()
  
      self._test_model_numerically(
          model,
          local_aot_ort,
          example_args_collection,
          fullgraph=True,
          test_backward=test_backward,
      )
  
      if test_local_backend:
          assert local_ort is not None
          number_of_captured_graphs = 2 if test_backward else 1
          execution_count = (
              len(example_args_collection) * number_of_captured_graphs
          )
          self._assert_counting_information(
              local_ort,
              expected_execution_count=execution_count,
              number_of_cached_graph_modules=number_of_captured_graphs,
              number_of_exported_onnx_models_for_all_graph_modules=(1,)
              * number_of_captured_graphs,
          )
  
imports: from __future__ import annotations
  import copy
  import dataclasses
  import os
  import sys
  from typing import Tuple
  import onnxruntime
  import torch
  import torch._dynamo.backends.registry
  from parameterized import parameterized
  from torch import nn
  from torch.onnx import _OrtBackend, _OrtBackendOptions, ExportOptions
  from torch.testing._internal import common_utils
  import onnx_test_common
label_desc: Use image node_id 13 for calling the TestDynamoWithONNXRuntime obj's underlying member method code with example usage: obj.test_llama_decoder_with_local_backend(test_local_backend, test_backward) and returns: hidden_state,  attention_mask,  position_ids, decoder_output