code: def test_stable_diffusion_prompt_embeds(self):
      pipe = OnnxStableDiffusionPipeline.from_pretrained(
          self.hub_checkpoint, provider="CPUExecutionProvider"
      )
      pipe.set_progress_bar_config(disable=None)
  
      inputs = self.get_dummy_inputs()
      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")]
  
      text_inputs = pipe.tokenizer(
          prompt,
          padding="max_length",
          max_length=pipe.tokenizer.model_max_length,
          truncation=True,
          return_tensors="np",
      )
      text_inputs = text_inputs["input_ids"]
  
      prompt_embeds = pipe.text_encoder(
          input_ids=text_inputs.astype(np.int32)
      )[0]
  
      inputs["prompt_embeds"] = prompt_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 8 for calling the OnnxStableDiffusionPipelineFastTests obj's underlying member method code with example usage: obj.test_stable_diffusion_prompt_embeds() without return types