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.
  
  Arguments
  ---------
  x: torch.Tensor
      the data tensor
  emb: torch.Tensor
      the embedding tensor
docstr_tok: ["Apply","the","module","to","`","x","`","given","`","emb","`","timestep","embeddings",".","Arguments","--","--","--","--","-","x",":","torch.Tensor","the","data","tensor","emb",":","torch.Tensor","the","embedding","tensor"]
code: def forward(self, x, emb=None):
      """
      Apply the module to `x` given `emb` timestep embeddings.
  
      Arguments
      ---------
      x: torch.Tensor
          the data tensor
      emb: torch.Tensor
          the embedding tensor
      """
  
imports: from abc import abstractmethod
  from speechbrain.utils.data_utils import pad_divisible
  from .autoencoders import NormalizingAutoencoder
  import math
  import torch
  import torch.nn
  import torch.nn.functional
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