class_docstr: Norm layer modified to incorporate timestep embeddings.
  
  Parameters:
      embedding_dim (`int`): The size of each embedding vector.
      num_embeddings (`int`): The size of the embeddings dictionary.
class_docstr_tok: ["Norm","layer","modified","to","incorporate","timestep","embeddings",".","Parameters",":","embedding_dim","(","`","int","`",")",":","The","size","of","each","embedding","vector",".","num_embeddings","(","`","int","`",")",":","The","size","of","the","embeddings","dictionary","."]
code: def __init__(self, embedding_dim: int, num_embeddings: int):
      super().__init__()
      self.emb = nn.Embedding(num_embeddings, embedding_dim)
      self.silu = nn.SiLU()
      self.linear = nn.Linear(embedding_dim, embedding_dim * 2)
      self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False)
  
imports: import numbers
  from typing import Dict, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from ..utils import is_torch_version
  from .activations import get_activation
  from .embeddings import CombinedTimestepLabelEmbeddings, PixArtAlphaCombinedTimestepSizeEmbeddings
label_desc: Use image node_id 1 to create a new AdaLayerNorm object from inherited base classes: nn with example: obj = AdaLayerNorm(embedding_dim, num_embeddings)