code: def decode_first_stage(self, z):
      z = 1.0 / self.scale_factor * z
      n_samples = default(
          self.en_and_decode_n_samples_a_time, z.shape[0]
      )
  
      n_rounds = math.ceil(z.shape[0] / n_samples)
      all_out = []
      with torch.autocast(
          "cuda", enabled=not self.disable_first_stage_autocast
      ):
          for n in range(n_rounds):
              if isinstance(
                  self.first_stage_model.decoder, VideoDecoder
              ):
                  kwargs = {
                      "timesteps": len(
                          z[n * n_samples : (n + 1) * n_samples]
                      )
                  }
              else:
                  kwargs = {}
              out = self.first_stage_model.decode(
                  z[n * n_samples : (n + 1) * n_samples], **kwargs
              )
              all_out.append(out)
      out = torch.cat(all_out, dim=0)
      return out
  
imports: import math
  from contextlib import contextmanager
  from typing import Any, Dict, List, Optional, Tuple, Union
  import pytorch_lightning
  import torch
  from omegaconf import ListConfig, OmegaConf
  from safetensors.torch import load_file
  from torch.optim.lr_scheduler import LambdaLR
  from ..modules import UNCONDITIONAL_CONFIG
  from ..modules.autoencoding.temporal_ae import VideoDecoder
  from ..modules.diffusionmodules.wrappers import OPENAIUNETWRAPPER
  from ..modules.ema import LitEma
  from ..util import default, disabled_train, get_obj_from_str, instantiate_from_config, log_txt_as_img
label_desc: Use image node_id 5 for calling the DiffusionEngine obj's underlying member method code with example usage: obj.decode_first_stage(z) and returns: out