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 prepare_control_cond(
      self,
      controlnet_cond,
      do_classifier_free_guidance,
      batch_size,
      num_images_per_prompt,
  ):
      processed_cond_list = []
      for cond in controlnet_cond:
          cond = np.stack([cond] * batch_size * num_images_per_prompt)
          if do_classifier_free_guidance:
              cond = np.concatenate([cond] * 2)
          processed_cond_list.append(cond)
      return processed_cond_list
  
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 7 for calling the CoreMLStableDiffusionPipeline obj's underlying member method code with example usage: obj.prepare_control_cond(controlnet_cond, do_classifier_free_guidance, batch_size, num_images_per_prompt) and returns: processed_cond_list