code: def decode(self, img_seq):
      self.log_codes(img_seq)
      if hasattr(self.codebook, "embed_code"):
          image_embeds = self.codebook.embed_code(img_seq)
      else:
          image_embeds = F.embedding(img_seq, self.codebook.codebook)
      b, n, d = image_embeds.shape
  
      kwargs = {}
      if self.positional_dims == 1:
          arrange = "b n d -> b d n"
      else:
          h = w = int(sqrt(n))
          arrange = "b (h w) d -> b d h w"
          kwargs = {"h": h, "w": w}
      image_embeds = rearrange(image_embeds, arrange, **kwargs)
      images = [image_embeds]
      for layer in self.decoder:
          images.append(layer(images[-1]))
      return images[-1], images[-2]
  
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 5 for calling the DiscreteVAE obj's underlying member method code with example usage: obj.decode(img_seq) and returns: images,  images