code: def ldm_transform_latent(z, extra_scale_factor=1):
      channel_means = [0.38862467, 0.02253063, 0.07381133, -0.0171294]
      channel_stds = [0.9654121, 1.0440036, 0.76147926, 0.77022034]
  
      if len(z.shape) != 4:
          raise ValueError()
  
      z = z * 0.18215
      channels = [z[:, i] for i in range(z.shape[1])]
  
      channels = [
          extra_scale_factor * (c - channel_means[i]) / channel_stds[i]
          for i, c in enumerate(channels)
      ]
      return torch.stack(channels, dim=1)
  
imports: import math
  import os
  import urllib
  import warnings
  from argparse import ArgumentParser
  import torch
  import torch.nn
  import torch.nn.functional
  from huggingface_hub.utils import insecure_hashlib
  from safetensors.torch import load_file
  from tqdm import tqdm
  from diffusers import AutoencoderKL, ConsistencyDecoderVAE, DiffusionPipeline, StableDiffusionPipeline, UNet2DModel
  from diffusers.models.autoencoders.vae import Encoder
  from diffusers.models.embeddings import TimestepEmbedding
  from diffusers.models.unet_2d_blocks import ResnetDownsampleBlock2D, ResnetUpsampleBlock2D, UNetMidBlock2D
label_desc: Use image node_id 3 for calling the ConsistencyDecoder obj's underlying member method code with example usage: obj.ldm_transform_latent(z, extra_scale_factor) and returns: torch