code: def __init__(
      self,
      d_model: int,
      n_head: int,
      attn_mask: torch.Tensor = None,
      drop_path: float = 0.0,
  ):
      super().__init__()
  
      self.attn = nn.MultiheadAttention(d_model, n_head)
      self.ln_1 = LayerNorm(d_model)
      self.mlp = nn.Sequential(
          OrderedDict(
              [
                  ("c_fc", nn.Linear(d_model, d_model * 4)),
                  ("gelu", QuickGELU()),
                  ("c_proj", nn.Linear(d_model * 4, d_model)),
              ]
          )
      )
      self.ln_2 = LayerNorm(d_model)
      self.attn_mask = attn_mask
      self.drop_path = (
          DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
      )
  
imports: import os
  from collections import OrderedDict
  import torch
  from mmcv.cnn.bricks import DropPath
  from torch import nn
  from transformers import CLIPTokenizer
  from .utils import get_prompt_templates
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, drop_path)