docstr: Args:
      x: the (reconstructed) latent embdeddings to be decoded to images.
      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.
          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}"
      B, T, C, H, W = x.shape
      x = x.reshape(B * T, C, H, W)
      if self._max_frames is not None and B * T > self._max_frames:
          assert (B * T) % self._max_frames == 0, "Can't reshape!"
          f = (B * T) // self._max_frames
          x = x.reshape(f, self._max_frames, C, H, W)
          x = torch.stack(
              [self.transforms(chunk) for chunk in x], dim=0
          )
          x = torch.flatten(x, start_dim=0, end_dim=1)
      else:
          x = self.transforms(x)
  
      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 ELICSynthesis obj's underlying member method code with example usage: obj.forward(x, frames_shape) and returns: x