code: def forward(
      self, x: torch.Tensor, conditioning_embedding: torch.Tensor
  ) -> torch.Tensor:
      emb = self.linear(self.silu(conditioning_embedding))
      scale, shift = torch.chunk(emb, 2, dim=1)
      x = self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :]
      return x
  
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 2 for calling the AdaLayerNormContinuous obj's underlying member method code with example usage: obj.forward(x, conditioning_embedding) and returns: x