class_docstr: GroupNorm 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.
      num_groups (`int`): The number of groups to separate the channels into.
      act_fn (`str`, *optional*, defaults to `None`): The activation function to use.
      eps (`float`, *optional*, defaults to `1e-5`): The epsilon value to use for numerical stability.
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code: def __init__(
      self,
      embedding_dim: int,
      out_dim: int,
      num_groups: int,
      act_fn: Optional[str] = None,
      eps: float = 1e-5,
  ):
      super().__init__()
      self.num_groups = num_groups
      self.eps = eps
  
      if act_fn is None:
          self.act = None
      else:
          self.act = get_activation(act_fn)
  
      self.linear = nn.Linear(embedding_dim, out_dim * 2)
  
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 AdaGroupNorm object from inherited base classes: nn with example: obj = AdaGroupNorm(embedding_dim, out_dim, num_groups, act_fn, eps)