code: def forward(self, x, timesteps, skip_time_block=False):
      if skip_time_block:
          return super().forward(x)
  
      x_in = x
      x = self.attention(x)
      h, w = x.shape[2:]
      x = rearrange(x, "b c h w -> b (h w) c")
  
      x_mix = x
      num_frames = torch.arange(timesteps, device=x.device)
      num_frames = repeat(
          num_frames, "t -> b t", b=x.shape[0] // timesteps
      )
      num_frames = rearrange(num_frames, "b t -> (b t)")
      t_emb = timestep_embedding(
          num_frames, self.in_channels, repeat_only=False
      )
      emb = self.video_time_embed(t_emb)  # b, n_channels
      emb = emb[:, None, :]
      x_mix = x_mix + emb
  
      alpha = self.get_alpha()
      x_mix = self.time_mix_block(x_mix, timesteps=timesteps)
      x = alpha * x + (1.0 - alpha) * x_mix  # alpha merge
  
      x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w)
      x = self.proj_out(x)
  
      return x_in + x
  
imports: from typing import Callable, Iterable, Union
  import torch
  from einops import rearrange, repeat
  from sgm.modules.diffusionmodules.model import XFORMERS_IS_AVAILABLE, AttnBlock, Decoder, MemoryEfficientAttnBlock, ResnetBlock
  from sgm.modules.diffusionmodules.openaimodel import ResBlock, timestep_embedding
  from sgm.modules.video_attention import VideoTransformerBlock
  from sgm.util import partialclass
label_desc: Use image node_id 2 for calling the MemoryEfficientVideoBlock obj's underlying member method code with example usage: obj.forward(x, timesteps, skip_time_block) and returns: unknown, super