code: def __init__(self, groups, normalized_shape, context_dim):
      super().__init__()
      self.norm = nn.GroupNorm(groups, normalized_shape, affine=False)
      self.context_mlp = nn.Sequential(
          nn.SiLU(), nn.Linear(context_dim, 2 * normalized_shape)
      )
      self.context_mlp[1].weight.data.zero_()
      self.context_mlp[1].bias.data.zero_()
  
imports: from dataclasses import dataclass
  from typing import Dict, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..utils import BaseOutput, logging
  from .attention_processor import Attention, AttentionProcessor, AttnProcessor
  from .embeddings import TimestepEmbedding, Timesteps
  from .modeling_utils import ModelMixin
label_desc: Use image node_id 1 to create a new Kandinsky3ConditionalGroupNorm object from inherited base classes: nn with example: obj = Kandinsky3ConditionalGroupNorm(groups, normalized_shape, context_dim)