code: def get_image_path(args, **override_kwargs):
      """mkdir output folder and encode metadata in the filename"""
      out_folder = os.path.join(
          args.o, "_".join(args.prompt.replace("/", "_").rsplit(" "))
      )
      os.makedirs(out_folder, exist_ok=True)
  
      out_fname = (
          f"randomSeed_{override_kwargs.get('seed', None) or args.seed}"
      )
      out_fname += f"_computeUnit_{override_kwargs.get('compute_unit', None) or args.compute_unit}"
      out_fname += f"_modelVersion_{override_kwargs.get('model_version', None) or args.model_version.replace('/', '_')}"
  
      if args.scheduler is not None:
          out_fname += f"_customScheduler_{override_kwargs.get('scheduler', None) or args.scheduler}"
          out_fname += f"_numInferenceSteps{override_kwargs.get('num_inference_steps', None) or args.num_inference_steps}"
  
      return os.path.join(out_folder, out_fname + ".png")
  
imports: import argparse
  from diffusers import StableDiffusionPipeline, StableDiffusionXLPipeline
  from diffusers.pipelines.pipeline_utils import DiffusionPipeline
  from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
  from diffusers.schedulers import DDIMScheduler, DPMSolverMultistepScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler, PNDMScheduler
  from diffusers.schedulers.scheduling_utils import SchedulerMixin
  import gc
  import inspect
  import logging
  import numpy
  import os
  from python_coreml_stable_diffusion.coreml_model import CoreMLModel, _load_mlpackage, _load_mlpackage_controlnet, get_available_compute_units
  import time
  import torch
  from transformers import CLIPFeatureExtractor, CLIPTokenizer
  from typing import List, Optional, Union, Tuple
  from PIL import Image
label_desc: Use image node_id 3 for calling a global function with example usage: get_image_path(args) and returns: os