class_docstr: ## Autoencoder
  
  This consists of the encoder and decoder modules.
docstr: ### Decode images from latent representation
  
  :param z: is the latent representation with shape `[batch_size, emb_channels, z_height, z_height]`
code: def decode(self, z: torch.Tensor):
      """
      ### Decode images from latent representation
  
      :param z: is the latent representation with shape `[batch_size, emb_channels, z_height, z_height]`
      """
      # Map to embedding space from the quantized representation
      z = self.post_quant_conv(z)
      # Decode the image of shape `[batch_size, channels, height, width]`
      return self.decoder(z)
  
imports: from typing import List
  import torch
  import torch.nn.functional
  from torch import nn
label_desc: Use image node_id 3 for calling the Autoencoder obj's underlying member method code with example usage: obj.decode(z) and returns: self