code: def __call__(
      self,
      features: torch.Tensor,
      schedule=[1.0, 0.5],
  ):
      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)
          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)
          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
  
file_path: consistencydecoder/consistencydecoder/__init__.py
imports: import hashlib
  import math
  import os
  import urllib
  import warnings
  import torch
  from tqdm import tqdm
label_desc: Use image node_id 4 for calling the ConsistencyDecoder obj's underlying member method code with example usage: obj.__call__(features, schedule) and returns: x_start