code: def test_negative_prompt(self, model_arch: str):
      pipeline = self.ORTMODEL_CLASS.from_pretrained(
          MODEL_NAMES[model_arch], export=True
      )
      inputs = _generate_inputs()
      inputs["height"], inputs["width"] = 64, 32
      negative_prompt = ["This is a negative prompt"]
      np.random.seed(0)
      image_slice_1 = pipeline(
          **inputs, negative_prompt=negative_prompt
      ).images[0, -3:, -3:, -1]
      prompt = inputs.pop("prompt")
      embeds = []
      for p in [prompt, negative_prompt]:
          text_inputs = pipeline.tokenizer(
              p,
              padding="max_length",
              max_length=pipeline.tokenizer.model_max_length,
              truncation=True,
              return_tensors="np",
          )
          text_inputs = text_inputs["input_ids"].astype(
              pipeline.text_encoder.input_dtype.get(
                  "input_ids", np.int32
              )
          )
          embeds.append(pipeline.text_encoder(text_inputs)[0])
  
      inputs["prompt_embeds"], inputs["negative_prompt_embeds"] = embeds
      np.random.seed(0)
      image_slice_2 = pipeline(**inputs).images[0, -3:, -3:, -1]
      self.assertTrue(
          np.allclose(image_slice_1, image_slice_2, atol=1e-4)
      )
  
imports: import random
  import unittest
  from typing import Dict
  import numpy
  import PIL
  import pytest
  import torch
  from diffusers import OnnxStableDiffusionImg2ImgPipeline, StableDiffusionPipeline, StableDiffusionXLPipeline
  from diffusers.utils import load_image
  from diffusers.utils.testing_utils import floats_tensor
  from packaging.version import Version, parse
  from parameterized import parameterized
  from transformers.testing_utils import require_torch_gpu
  from utils_onnxruntime_tests import MODEL_NAMES, SEED, ORTModelTestMixin
  from optimum.onnxruntime import ORTLatentConsistencyModelPipeline, ORTStableDiffusionImg2ImgPipeline, ORTStableDiffusionInpaintPipeline, ORTStableDiffusionPipeline, ORTStableDiffusionXLImg2ImgPipeline, ORTStableDiffusionXLPipeline
  from optimum.onnxruntime.modeling_diffusion import ORTModelTextEncoder, ORTModelUnet, ORTModelVaeDecoder, ORTModelVaeEncoder
  from optimum.pipelines.diffusers.pipeline_utils import VaeImageProcessor
  from optimum.utils.import_utils import _diffusers_version
  from optimum.utils.testing_utils import grid_parameters, require_diffusers, require_ort_rocm
label_desc: Use image node_id 3 for calling the ORTStableDiffusionPipelineTest obj's underlying member method code with example usage: obj.test_negative_prompt(model_arch) without return types