code: def __init__(
      self,
      embedding_dim: int,
      conditioning_embedding_dim: int,
      # NOTE: It is a bit weird that the norm layer can be configured to have scale and shift parameters
      # because the output is immediately scaled and shifted by the projected conditioning embeddings.
      # Note that AdaLayerNorm does not let the norm layer have scale and shift parameters.
      # However, this is how it was implemented in the original code, and it's rather likely you should
      # set `elementwise_affine` to False.
      elementwise_affine=True,
      eps=1e-5,
      bias=True,
      norm_type="layer_norm",
  ):
      super().__init__()
      self.silu = nn.SiLU()
      self.linear = nn.Linear(
          conditioning_embedding_dim, embedding_dim * 2, bias=bias
      )
      if norm_type == "layer_norm":
          self.norm = LayerNorm(
              embedding_dim, eps, elementwise_affine, bias
          )
      elif norm_type == "rms_norm":
          self.norm = RMSNorm(embedding_dim, eps, elementwise_affine)
      else:
          raise ValueError(f"unknown norm_type {norm_type}")
  
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 AdaLayerNormContinuous object from inherited base classes: nn with example: obj = AdaLayerNormContinuous(embedding_dim, conditioning_embedding_dim, elementwise_affine, eps, bias, norm_type)