code: def __init__(
      self,
      in_channels: int,
      alpha: float = 0,
      merge_strategy: str = "learned",
  ):
      super().__init__(in_channels)
      # no context, single headed, as in base class
      self.time_mix_block = VideoTransformerBlock(
          dim=in_channels,
          n_heads=1,
          d_head=in_channels,
          checkpoint=False,
          ff_in=True,
          attn_mode="softmax-xformers",
      )
  
      time_embed_dim = self.in_channels * 4
      self.video_time_embed = torch.nn.Sequential(
          torch.nn.Linear(self.in_channels, time_embed_dim),
          torch.nn.SiLU(),
          torch.nn.Linear(time_embed_dim, self.in_channels),
      )
  
      self.merge_strategy = merge_strategy
      if self.merge_strategy == "fixed":
          self.register_buffer("mix_factor", torch.Tensor([alpha]))
      elif self.merge_strategy == "learned":
          self.register_parameter(
              "mix_factor", torch.nn.Parameter(torch.Tensor([alpha]))
          )
      else:
          raise ValueError(
              f"unknown merge strategy {self.merge_strategy}"
          )
  
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 1 to create a new MemoryEfficientVideoBlock object from inherited base classes: MemoryEfficientAttnBlock with example: obj = MemoryEfficientVideoBlock(in_channels, alpha, merge_strategy)