code: def test_num_images_per_prompt(self, model_arch: str):
      model_args = {"test_name": model_arch, "model_arch": model_arch}
      self._setup(model_args)
      pipeline = self.ORTMODEL_CLASS.from_pretrained(
          self.onnx_model_dirs[model_arch]
      )
      self.assertEqual(pipeline.vae_scale_factor, 2)
      self.assertEqual(
          pipeline.vae_decoder.config["latent_channels"], 4
      )
      self.assertEqual(pipeline.unet.config["in_channels"], 4)
  
      batch_size, height = 1, 32
      for width in [64, 32]:
          inputs = self.generate_inputs(
              height=height, width=width, batch_size=batch_size
          )
          for num_images in [1, 3]:
              outputs = pipeline(
                  **inputs, num_images_per_prompt=num_images
              ).images
              self.assertEqual(
                  outputs.shape,
                  (batch_size * num_images, height, width, 3),
              )
  
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 2 for calling the ORTStableDiffusionPipelineBase obj's underlying member method code with example usage: obj.test_num_images_per_prompt(model_arch) without return types