code: def __call__(
      self,
      features: torch.Tensor,
      schedule=[1.0, 0.5],
      generator=None,
  ):
      features = self.ldm_transform_latent(features)
      ts = self.round_timesteps(
          torch.arange(0, 1024),
          1024,
          self.n_distilled_steps,
          truncate_start=False,
      )
      shape = (
          features.size(0),
          3,
          8 * features.size(2),
          8 * features.size(3),
      )
      x_start = torch.zeros(
          shape, device=features.device, dtype=features.dtype
      )
      schedule_timesteps = [int((1024 - 1) * s) for s in schedule]
      for i in schedule_timesteps:
          t = ts[i].item()
          t_ = torch.tensor([t] * features.shape[0]).to(self.device)
          # noise = torch.randn_like(x_start)
          noise = torch.randn(
              x_start.shape, dtype=x_start.dtype, generator=generator
          ).to(device=x_start.device)
          x_start = (
              _extract_into_tensor(
                  self.sqrt_alphas_cumprod, t_, x_start.shape
              )
              * x_start
              + _extract_into_tensor(
                  self.sqrt_one_minus_alphas_cumprod, t_, x_start.shape
              )
              * noise
          )
          c_in = _extract_into_tensor(self.c_in, t_, x_start.shape)
  
          import torch.nn.functional as F
  
          from diffusers import UNet2DModel
  
          if isinstance(self.ckpt, UNet2DModel):
              input = torch.concat(
                  [
                      c_in * x_start,
                      F.upsample_nearest(features, scale_factor=8),
                  ],
                  dim=1,
              )
              model_output = self.ckpt(input, t_).sample
          else:
              model_output = self.ckpt(
                  c_in * x_start, t_, features=features
              )
  
          B, C = x_start.shape[:2]
          model_output, _ = torch.split(model_output, C, dim=1)
          pred_xstart = (
              _extract_into_tensor(self.c_out, t_, x_start.shape)
              * model_output
              + _extract_into_tensor(self.c_skip, t_, x_start.shape)
              * x_start
          ).clamp(-1, 1)
          x_start = pred_xstart
      return x_start
  
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 4 for calling the ConsistencyDecoder obj's underlying member method code with example usage: obj.__call__(features, schedule, generator) and returns: x_start