class_docstr: Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly avoids costly matrix
  multiplications and allows for post-hoc remapping of indices.
code: def __init__(
      self,
      n_e: int,
      vq_embed_dim: int,
      beta: float,
      remap=None,
      unknown_index: str = "random",
      sane_index_shape: bool = False,
      legacy: bool = True,
  ):
      super().__init__()
      self.n_e = n_e
      self.vq_embed_dim = vq_embed_dim
      self.beta = beta
      self.legacy = legacy
  
      self.embedding = nn.Embedding(self.n_e, self.vq_embed_dim)
      self.embedding.weight.data.uniform_(
          -1.0 / self.n_e, 1.0 / self.n_e
      )
  
      self.remap = remap
      if self.remap is not None:
          self.register_buffer(
              "used", torch.tensor(np.load(self.remap))
          )
          self.used: torch.Tensor
          self.re_embed = self.used.shape[0]
          self.unknown_index = (
              unknown_index  # "random" or "extra" or integer
          )
          if self.unknown_index == "extra":
              self.unknown_index = self.re_embed
              self.re_embed = self.re_embed + 1
          print(
              f"Remapping {self.n_e} indices to {self.re_embed} indices. "
              f"Using {self.unknown_index} for unknown indices."
          )
      else:
          self.re_embed = n_e
  
      self.sane_index_shape = sane_index_shape
  
imports: from dataclasses import dataclass
  from typing import Optional, Tuple
  import numpy
  import torch
  import torch.nn
  from ...utils import BaseOutput, is_torch_version
  from ...utils.torch_utils import randn_tensor
  from ..activations import get_activation
  from ..attention_processor import SpatialNorm
  from ..unet_2d_blocks import AutoencoderTinyBlock, UNetMidBlock2D, get_down_block, get_up_block
label_desc: Use image node_id 1 to create a new VectorQuantizer object from inherited base classes: nn with example: obj = VectorQuantizer(n_e, vq_embed_dim, beta, remap, unknown_index, sane_index_shape, legacy)