class_docstr: Core ML version of
  `diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline`
class_docstr_tok: ["Core","ML","version","of","`","diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline","`"]
code: def check_inputs(self, prompt, height, width, callback_steps):
      if height != self.height or width != self.width:
          logger.warning(
              "`height` and `width` dimensions (of the output image tensor) are fixed when exporting the Core ML models "
              "unless flexible shapes are used during export (https://coremltools.readme.io/docs/flexible-inputs). "
              "This pipeline was provided with Core ML models that generate {self.height}x{self.width} images (user requested {height}x{width})"
          )
  
      if not isinstance(prompt, str) and not isinstance(prompt, list):
          raise ValueError(
              f"`prompt` has to be of type `str` or `list` but is {type(prompt)}"
          )
  
      if height % 8 != 0 or width % 8 != 0:
          raise ValueError(
              f"`height` and `width` have to be divisible by 8 but are {height} and {width}."
          )
  
      if (callback_steps is None) or (
          callback_steps is not None
          and (
              not isinstance(callback_steps, int) or callback_steps <= 0
          )
      ):
          raise ValueError(
              f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
              f" {type(callback_steps)}."
          )
  
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 8 for calling the CoreMLStableDiffusionPipeline obj's underlying member method code with example usage: obj.check_inputs(prompt, height, width, callback_steps) without return types