code: def __init__(
      self,
      dim,
      n_embed,
      decay=0.99,
      eps=1e-5,
      balancing_heuristic=False,
      new_return_order=False,
  ):
      super().__init__()
  
      self.dim = dim
      self.n_embed = n_embed
      self.decay = decay
      self.eps = eps
  
      self.balancing_heuristic = balancing_heuristic
      self.codes = None
      self.max_codes = 64000
      self.codes_full = False
      self.new_return_order = new_return_order
  
      embed = torch.randn(dim, n_embed)
      self.register_buffer("embed", embed)
      self.register_buffer("cluster_size", torch.zeros(n_embed))
      self.register_buffer("embed_avg", embed.clone())
  
imports: import functools
  from math import sqrt
  import torch
  import torch.distributed
  import torch.nn
  import torch.nn.functional
  import torchaudio
  from einops import rearrange
label_desc: Use image node_id 1 to create a new Quantize object from inherited base classes: nn with example: obj = Quantize(dim, n_embed, decay, eps, balancing_heuristic, new_return_order)