code: def __init__(
      self, d_model: int, n_head: int, attn_mask: paddle.Tensor = None
  ):
      super().__init__()
      self.attn = MultiHeadAttention(d_model, n_head)
      self.ln_1 = nn.LayerNorm(d_model)
      self.mlp = nn.Sequential(
          *[
              ("c_fc", nn.Linear(d_model, d_model * 4)),
              ("gelu", QuickGELU()),
              ("c_proj", nn.Linear(d_model * 4, d_model)),
          ]
      )
      self.ln_2 = nn.LayerNorm(d_model)
      self.attn_mask = attn_mask
  
imports: from collections import OrderedDict
  from typing import Tuple
  from typing import Union
  import numpy
  import paddle
  import paddle.nn.functional
  from disco_diffusion_cnclip_vitb16.cn_clip.clip import _tokenizer
  from disco_diffusion_cnclip_vitb16.cn_clip.clip.configuration_bert import BertConfig
  from disco_diffusion_cnclip_vitb16.cn_clip.clip.modeling_bert import BertModel
  from paddle import nn
  from paddle.nn import MultiHeadAttention
label_desc: Use image node_id 1 to create a new ResidualAttentionBlock object from inherited base classes: nn with example: obj = ResidualAttentionBlock(d_model, n_head, attn_mask)