docstr: Args:
      x: the (reconstructed) latent embdeddings to be decoded to images,
          expected shape [B, T, C, H, W]
      frames_shape: shape of the video clip to be reconstructed.
  Returns:
      reconstruction: reconstruction of the original video clip with shape
          [B, T, C, H, W] = frames_shape.
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code: def forward(self, x: Tensor, frames_shape: torch.Size) -> Tensor:
      """
      Args:
          x: the (reconstructed) latent embdeddings to be decoded to images,
              expected shape [B, T, C, H, W]
          frames_shape: shape of the video clip to be reconstructed.
      Returns:
          reconstruction: reconstruction of the original video clip with shape
              [B, T, C, H, W] = frames_shape.
      """
      assert x.dim() == 5, f"Expected [B, T, C, H, W] got {x.shape}"
      # Treat T as part of the Batch dimension, storing values to reshape back
      B, T, *_ = x.shape
      x = x.reshape(-1, *x.shape[2:])
      assert len(frames_shape) == 5
  
      x = self.transforms(x)  # final reconstruction
      x = x.reshape(B, T, *x.shape[1:])
      return x[..., : frames_shape[-2], : frames_shape[-1]]
  
imports: from typing import List, Optional
  import torch
  import torch.nn
  from compressai.layers import GDN, AttentionBlock
  from neural.layers_utils import make_conv, make_deconv
  from torch import Tensor
label_desc: Use image node_id 3 for calling the ConvGDNSynthesis obj's underlying member method code with example usage: obj.forward(x, frames_shape) and returns: x