code: def __init__(
      self, d_model: int, n_head: int, attn_mask: torch.Tensor = None
  ):
      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
  
imports: from collections import OrderedDict
  import timm
  from torch import nn
  from lavis.models.ulip_models import losses
  from torch.nn.parameter import Parameter
  from easydict import EasyDict
  import torch
  import numpy
  from lavis.common.dist_utils import download_cached_file
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)