class_docstr: Glue together encoder transform, entropy model and decoder transform
code: def decode_Pscene(
      self, bottleneck: Bottleneck, state: State, cache: memoize.Cache
  ) -> Tuple[Tensor, State]:
      latent_q = bottleneck.latent_q  # [B, C, H, W]
      synthesis_in = self._dequantizer(
          latent_q=latent_q,
          entropy_features=bottleneck.entropy_model_features,
      )  # [B, H, W, C] this is the tensor we pass through image synthesis
  
      # Preprocess current quantized latent, `latent_q`.
      next_state_entry = self.entropy_model.process_previous_latent_q(
          latent_q
      )
      new_state = (*state, next_state_entry)
      new_state = new_state[0 - self._context_len :]
      # NB: as in decode_Iscene, we return synthesis_in instead of reconstruction and
      # apply image_synthesis in the forward pass
      return synthesis_in, new_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 6 for calling the VCTPipeline obj's underlying member method code with example usage: obj.decode_Pscene(bottleneck, state, cache) and returns: synthesis_in,  new_state