code: def run_pipe(pipe):
      if torch.backends.mps.is_available():
          device = "mps"
      elif torch.cuda.is_available():
          device = "cuda"
      else:
          device = "cpu"
      logger.debug(f"Placing pipe in {device}")
  
      global rng, rng_state
      rng.set_state(rng_state)
      kwargs = dict(
          prompt=RANDOM_TEST_DATA,
          negative_prompt=[""] * len(RANDOM_TEST_DATA),
          num_inference_steps=1,
          height=TEST_RESOLUTION,
          width=TEST_RESOLUTION,
          output_type="latent",
          generator=rng,
      )
      if "Img2Img" in pipe.__class__.__name__:
          kwargs["image"] = RANDOM_TEST_IMAGE_DATA
          kwargs.pop("height")
          kwargs.pop("width")
  
          # Run a single denoising step
          kwargs["num_inference_steps"] = 4
          kwargs["strength"] = 0.25
  
      return np.array(
          [
              latent.cpu().numpy()
              for latent in pipe.to(device)(**kwargs).images
          ]
      )
  
imports: from collections import OrderedDict
  from copy import deepcopy
  from functools import partial
  import argparse
  import gc
  import json
  import logging
  import numpy
  import os
  from PIL import Image
  from python_coreml_stable_diffusion.torch2coreml import compute_psnr, get_pipeline
  import time
  import torch
  import torch.nn
  import requests
  from tqdm import tqdm, trange
  from coremltools.converters.mil.mil import types
label_desc: Use image node_id 8 for calling a global function with example usage: run_pipe(pipe) and returns: np