code: def __init__(
      self,
      out_channels,
      *args,
      dropout=0.0,
      video_kernel_size=3,
      alpha=0.0,
      merge_strategy="learned",
      **kwargs,
  ):
      super().__init__(
          out_channels=out_channels, dropout=dropout, *args, **kwargs
      )
      if video_kernel_size is None:
          video_kernel_size = [3, 1, 1]
      self.time_stack = ResBlock(
          channels=out_channels,
          emb_channels=0,
          dropout=dropout,
          dims=3,
          use_scale_shift_norm=False,
          use_conv=False,
          up=False,
          down=False,
          kernel_size=video_kernel_size,
          use_checkpoint=False,
          skip_t_emb=True,
      )
  
      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 VideoResBlock object from inherited base classes: ResnetBlock with example: obj = VideoResBlock(out_channels)