code: def decode(self, z: torch.Tensor, **decoder_kwargs) -> torch.Tensor:
      if self.max_batch_size is None:
          dec = self.post_quant_conv(z)
          dec = self.decoder(dec, **decoder_kwargs)
      else:
          N = z.shape[0]
          bs = self.max_batch_size
          n_batches = int(math.ceil(N / bs))
          dec = list()
          for i_batch in range(n_batches):
              dec_batch = self.post_quant_conv(
                  z[i_batch * bs : (i_batch + 1) * bs]
              )
              dec_batch = self.decoder(dec_batch, **decoder_kwargs)
              dec.append(dec_batch)
          dec = torch.cat(dec, 0)
  
      return dec
  
imports: import logging
  import math
  import re
  from abc import abstractmethod
  from contextlib import contextmanager
  from typing import Any, Dict, List, Optional, Tuple, Union
  import pytorch_lightning
  import torch
  import torch.nn
  from einops import rearrange
  from packaging import version
  from ..modules.autoencoding.regularizers import AbstractRegularizer
  from ..modules.ema import LitEma
  from ..util import default, get_nested_attribute, get_obj_from_str, instantiate_from_config
label_desc: Use image node_id 4 for calling the AutoencodingEngineLegacy obj's underlying member method code with example usage: obj.decode(z) and returns: dec