code: def forward(self, x: Tensor) -> Tensor:
      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, *x.shape[1:])
          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)
      return x.reshape(B, T, *x.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 ELICAnalysis obj's underlying member method code with example usage: obj.forward(x) and returns: x