code: def encode(self, x) -> torch.Tensor:
      assert (
          not self.training
      ), f"{self.__class__.__name__} only supports inference currently"
      _, log = self.model.encode(x, **self.encoder_kwargs)
      assert isinstance(log, dict)
      inds = log["min_encoding_indices"]
      return rearrange(inds, "b ... -> b (...)")
  
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 2 for calling the AEIntegerWrapper obj's underlying member method code with example usage: obj.encode(x) and returns: rearrange