code: def test_stable_diffusion_negative_prompt_embeds(self):
      pipe = OnnxStableDiffusionPipeline.from_pretrained(
          self.hub_checkpoint, provider="CPUExecutionProvider"
      )
      pipe.set_progress_bar_config(disable=None)
  
      inputs = self.get_dummy_inputs()
      negative_prompt = 3 * ["this is a negative prompt"]
      inputs["negative_prompt"] = negative_prompt
      inputs["prompt"] = 3 * [inputs["prompt"]]
  
      # forward
      output = pipe(**inputs)
      image_slice_1 = output.images[0, -3:, -3:, -1]
  
      inputs = self.get_dummy_inputs()
      prompt = 3 * [inputs.pop("prompt")]
  
      embeds = []
      for p in [prompt, negative_prompt]:
          text_inputs = pipe.tokenizer(
              p,
              padding="max_length",
              max_length=pipe.tokenizer.model_max_length,
              truncation=True,
              return_tensors="np",
          )
          text_inputs = text_inputs["input_ids"]
  
          embeds.append(
              pipe.text_encoder(input_ids=text_inputs.astype(np.int32))[
                  0
              ]
          )
  
      inputs["prompt_embeds"], inputs["negative_prompt_embeds"] = embeds
  
      # forward
      output = pipe(**inputs)
      image_slice_2 = output.images[0, -3:, -3:, -1]
  
      assert (
          np.abs(
              image_slice_1.flatten() - image_slice_2.flatten()
          ).max()
          < 1e-4
      )
  
imports: import tempfile
  import unittest
  import numpy
  from diffusers import DDIMScheduler, DPMSolverMultistepScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler, OnnxStableDiffusionPipeline, PNDMScheduler
  from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu
  from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
label_desc: Use image node_id 9 for calling the OnnxStableDiffusionPipelineFastTests obj's underlying member method code with example usage: obj.test_stable_diffusion_negative_prompt_embeds() without return types