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 decode_latents(self, latents):
      latents = 1 / 0.18215 * latents
      dtype = self.vae_decoder.expected_inputs["z"]["dtype"]
      image = self.vae_decoder(z=latents.astype(dtype))["image"]
      image = np.clip(image / 2 + 0.5, 0, 1)
      image = image.transpose((0, 2, 3, 1))
  
      return image
  
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 5 for calling the CoreMLStableDiffusionPipeline obj's underlying member method code with example usage: obj.decode_latents(latents) and returns: image