class_docstr: Glue together encoder transform, entropy model and decoder transform
code: def decode_Iscene(
      self, bottleneck: Bottleneck
  ) -> Tuple[Tensor, State]:
      latent_q = bottleneck.latent_q  # [B, C, H, W]
      synthesis_in = self._dequantizer(
          latent_q=latent_q,  # .permute(0, 2, 3, 1).contiguous(),  # [B, H, W, C]
          entropy_features=bottleneck.entropy_model_features,
      )  # [B, C, H, W] this is the tensor we pass through image synthesis
      latent_q = bottleneck.latent_q  # noised
      latent_q = latent_q.detach()
      previous_latent = self.entropy_model.process_previous_latent_q(
          latent_q
      )
      # Note that this is a tuple, we start with a 1-length context.
      state: State = (previous_latent,)
  
      # NB: we apply the transforms (analysis and synthesis) in the forward pass onthe
      # entire video (TODO need to improve efficiency so as to handle long videos), so
      # here we return synthesis_in instead of reconstruction and apply image_synthesis
      return synthesis_in, state
  
imports: import itertools
  import math
  from typing import Generator, List, NamedTuple, Optional, Sequence, Tuple, Union
  import torch
  import torch.nn
  import utils.memoize
  from datamodules.video_data_api import Scenes, VideoData
  from neural.entropy_model import PreviousLatent, VCTEntropyModel
  from torch import Tensor
label_desc: Use image node_id 5 for calling the VCTPipeline obj's underlying member method code with example usage: obj.decode_Iscene(bottleneck) and returns: synthesis_in,  state