class_docstr: Residual Attention Block
  Args:
      d_model (int): dimension of model
      n_head (int): number of head
      attn_mask (Union[torch.Tensor, Callable]): mask for attention
      vis (int): visualization
      patch_nums (int): number of patches
      is_bridge_former_video (bool): text transformer or visual transformer for a single frame
code: def attention(self, x: torch.Tensor):
      attn_mask_ = self.attn_mask
      if self.attn_mask is not None and hasattr(
          self.attn_mask, "__call__"
      ):
          attn_mask_ = self.attn_mask(x.size(0))  # LND
  
      attn_mask_ = (
          attn_mask_.to(dtype=x.dtype, device=x.device)
          if attn_mask_ is not None
          else None
      )
      if self.vis:
          return self.attn(
              x,
              x,
              x,
              need_weights=False,
              attn_mask=attn_mask_,
              attention_probs_forward_hook=self.set_attn_probs,
              attention_probs_backwards_hook=self.set_attn_grad,
          )[0]
      else:
          return self.attn(
              x, x, x, need_weights=False, attn_mask=attn_mask_
          )[0]
  
imports: import os
  import warnings
  from collections import OrderedDict
  from typing import Tuple, Union, Callable
  import numpy
  from PIL import Image
  import torch
  from torch import nn
  from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
  from towhee.models.utils.download import download_from_url
  from towhee.models.clip.clip_utils import get_configs, convert_weights, patch_device, patch_float, tokenize
  from towhee.models.clip.auxilary import multi_head_attention_forward, MultiheadAttention
label_desc: Use image node_id 4 for calling the ResidualAttentionBlock obj's underlying member method code with example usage: obj.attention(x) and returns: self, self