code: def __init__(
      self,
      dim,
      num_heads=8,
      qkv_bias=True,
      use_rel_pos=False,
      rel_pos_zero_init=True,
      input_size=None,
  ):
      super().__init__()
      self.num_heads = num_heads
      head_dim = dim // num_heads
      self.scale = head_dim**-0.5
  
      self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
      self.proj = nn.Linear(dim, dim)
  
      self.use_rel_pos = use_rel_pos
      if self.use_rel_pos:
          # initialize relative positional embeddings
          self.rel_pos_h = nn.Parameter(
              torch.zeros(2 * input_size[0] - 1, head_dim)
          )
          self.rel_pos_w = nn.Parameter(
              torch.zeros(2 * input_size[1] - 1, head_dim)
          )
  
          if not rel_pos_zero_init:
              nn.init.trunc_normal_(self.rel_pos_h, std=0.02)
              nn.init.trunc_normal_(self.rel_pos_w, std=0.02)
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import build_activation_layer, build_norm_layer
  from mmcv.cnn.bricks import DropPath
  from mmengine.logging import MMLogger
  from mmengine.model import BaseModule
  from mmengine.runner.checkpoint import CheckpointLoader
  from mmdet.registry import MODELS
label_desc: Use image node_id 1 to create a new Attention object from inherited base classes: nn with example: obj = Attention(dim, num_heads, qkv_bias, use_rel_pos, rel_pos_zero_init, input_size)