code: def __init__(
      self, d_model: int, n_head: int, attn_mask: paddle.Tensor = None
  ):
      super().__init__()
      self.attn = paddle.nn.MultiHeadAttention(
          d_model,
          n_head,
          need_weights=False,
      )
      self.ln_1 = paddle.nn.LayerNorm(d_model)
      self.mlp = paddle.nn.Sequential(
          *[
              (
                  "c_fc",
                  paddle.nn.Linear(
                      in_features=d_model, out_features=d_model * 4
                  ),
              ),
              ("gelu", QuickGELU()),
              (
                  "c_proj",
                  paddle.nn.Linear(
                      in_features=d_model * 4, out_features=d_model
                  ),
              ),
          ]
      )
      self.ln_2 = paddle.nn.LayerNorm(d_model)
      self.attn_mask = attn_mask
  
imports: from collections import OrderedDict
  from typing import Tuple, Union
  import numpy
  import paddle
  from paddle.nn.initializer import Constant
  from paddle.vision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
label_desc: Use image node_id 1 to create a new ResidualAttentionBlock object from inherited base classes: paddle.nn with example: obj = ResidualAttentionBlock(d_model, n_head, attn_mask)