class_docstr: An implementation for Vector Quantization
class_docstr_tok: ["An","implementation","for","Vector","Quantization"]
docstr: Decodes the embedded ID by transposing the dimensions.
  
  This method decodes the embedded ID by applying a transpose operation to the dimensions of the
  output tensor from the `embed_code` method.
  
  Parameters
  ----------
  embed_id : torch.Tensor
      The tensor containing embedded IDs.
  
  Returns
  -------
  torch.Tensor
      The decoded tensor
docstr_tok: ["Decodes","the","embedded","ID","by","transposing","the","dimensions",".","This","method","decodes","the","embedded","ID","by","applying","a","transpose","operation","to","the","dimensions","of","the","output","tensor","from","the","`","embed_code","`","method",".","Parameters","--","--","--","--","--","embed_id",":","torch.Tensor","The","tensor","containing","embedded","IDs",".","Returns","--","--","--","-","torch.Tensor","The","decoded","tensor"]
code: def decode_code(self, embed_id: torch.Tensor):
      """
      Decodes the embedded ID by transposing the dimensions.
  
      This method decodes the embedded ID by applying a transpose operation to the dimensions of the
      output tensor from the `embed_code` method.
  
      Parameters
      ----------
      embed_id : torch.Tensor
          The tensor containing embedded IDs.
  
      Returns
      -------
      torch.Tensor
          The decoded tensor
      """
      return self.embed_code(embed_id).transpose(1, 2)
  
imports: import math
  from pathlib import Path
  from typing import List, Union
  import logging
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.utils.parametrizations import weight_norm
label_desc: Use image node_id 4 for calling the VectorQuantize obj's underlying member method code with example usage: obj.decode_code(embed_id) and returns: self