code: def encode_first_stage(self, x):
      n_samples = default(
          self.en_and_decode_n_samples_a_time, x.shape[0]
      )
      n_rounds = math.ceil(x.shape[0] / n_samples)
      all_out = []
      with torch.autocast(
          "cuda", enabled=not self.disable_first_stage_autocast
      ):
          for n in range(n_rounds):
              out = self.first_stage_model.encode(
                  x[n * n_samples : (n + 1) * n_samples]
              )
              all_out.append(out)
      z = torch.cat(all_out, dim=0)
      z = self.scale_factor * z
      return z
  
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 6 for calling the DiffusionEngine obj's underlying member method code with example usage: obj.encode_first_stage(x) and returns: z