code: def decode(
      self, inds: torch.Tensor, shape: Union[None, tuple, list] = None
  ) -> torch.Tensor:
      # expect inds shape (b, s) with s = h*w
      shape = default(shape, self.shape)  # Optional[(h, w)]
      if shape is not None:
          assert len(shape) == 2, f"Unhandeled shape {shape}"
          inds = rearrange(
              inds, "b (h w) -> b h w", h=shape[0], w=shape[1]
          )
      h = self.regularization.get_codebook_entry(inds)  # (b, h, w, c)
      h = rearrange(h, "b h w c -> b c h w")
      return self.model.decode(h)
  
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 3 for calling the AEIntegerWrapper obj's underlying member method code with example usage: obj.decode(inds, shape) and returns: self