class_docstr: Any module where forward() takes timestep embeddings as a second argument.
class_docstr_tok: ["Any","module","where","forward","(",")","takes","timestep","embeddings","as","a","second","argument","."]
docstr: Apply the module to `x` given `emb` timestep embeddings.
docstr_tok: ["Apply","the","module","to","`","x","`","given","`","emb","`","timestep","embeddings","."]
code: def forward(self, x: th.Tensor, emb: th.Tensor):
      """
      Apply the module to `x` given `emb` timestep embeddings.
      """
  
imports: import logging
  import math
  from abc import abstractmethod
  from typing import Iterable, List, Optional, Tuple, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from einops import rearrange
  from torch.utils.checkpoint import checkpoint
  from ...modules.attention import SpatialTransformer
  from ...modules.diffusionmodules.util import avg_pool_nd, conv_nd, linear, normalization, timestep_embedding, zero_module
  from ...modules.video_attention import SpatialVideoTransformer
  from ...util import exists
label_desc: Use image node_id 1 for calling the TimestepBlock obj's underlying member method code with example usage: obj.forward(x, emb) without return types