code: def forward(
      self,
      x: th.Tensor,
      timesteps: th.Tensor,
      context: Optional[th.Tensor] = None,
      y: Optional[th.Tensor] = None,
      time_context: Optional[th.Tensor] = None,
      num_video_frames: Optional[int] = None,
      image_only_indicator: Optional[th.Tensor] = None,
  ):
      assert (y is not None) == (
          self.num_classes is not None
      ), "must specify y if and only if the model is class-conditional -> no, relax this TODO"
      hs = []
      t_emb = timestep_embedding(
          timesteps, self.model_channels, repeat_only=False
      )
      emb = self.time_embed(t_emb)
  
      if self.num_classes is not None:
          assert y.shape[0] == x.shape[0]
          emb = emb + self.label_emb(y)
  
      h = x
      for module in self.input_blocks:
          h = module(
              h,
              emb,
              context=context,
              image_only_indicator=image_only_indicator,
              time_context=time_context,
              num_video_frames=num_video_frames,
          )
          hs.append(h)
      h = self.middle_block(
          h,
          emb,
          context=context,
          image_only_indicator=image_only_indicator,
          time_context=time_context,
          num_video_frames=num_video_frames,
      )
      for module in self.output_blocks:
          h = th.cat([h, hs.pop()], dim=1)
          h = module(
              h,
              emb,
              context=context,
              image_only_indicator=image_only_indicator,
              time_context=time_context,
              num_video_frames=num_video_frames,
          )
      h = h.type(x.dtype)
      return self.out(h)
  
file_path: generative-models/sgm/modules/diffusionmodules/video_model.py
imports: from functools import partial
  from typing import List, Optional, Union
  from einops import rearrange
  from ...modules.diffusionmodules.openaimodel import *
  from ...modules.video_attention import SpatialVideoTransformer
  from ...util import default
  from .util import AlphaBlender
label_desc: Use image node_id 2 for calling the VideoUNet obj's underlying member method code with example usage: obj.forward(x, timesteps, context, y, time_context, num_video_frames, image_only_indicator) and returns: self