code: def __init__(
      self,
      dim,
      num_heads=8,
      qkv_bias=False,
      qk_scale=None,
      attn_drop=0.0,
      proj_drop=0.0,
      window_size=None,
  ):
      super().__init__()
      self.num_heads = num_heads
      head_dim = dim // num_heads
      self.scale = qk_scale or head_dim**-0.5
  
      self.qkv = nn.Linear(dim, dim * 3, bias_attr=False)
  
      if qkv_bias:
          self.q_bias = self.create_parameter(
              shape=([dim]), default_initializer=zeros_
          )
          self.v_bias = self.create_parameter(
              shape=([dim]), default_initializer=zeros_
          )
      else:
          self.q_bias = None
          self.v_bias = None
      if window_size:
          self.window_size = window_size
          self.num_relative_distance = (2 * window_size[0] - 1) * (
              2 * window_size[1] - 1
          ) + 3
          self.relative_position_bias_table = self.create_parameter(
              shape=(self.num_relative_distance, num_heads),
              default_initializer=zeros_,
          )  # 2*Wh-1 * 2*Ww-1, nH
  
          coords_h = paddle.arange(window_size[0])
          coords_w = paddle.arange(window_size[1])
          coords = paddle.stack(
              paddle.meshgrid([coords_h, coords_w])
          )  # 2, Wh, Ww
          coords_flatten = paddle.flatten(coords, 1)  # 2, Wh*Ww
          coords_flatten_1 = paddle.unsqueeze(coords_flatten, 2)
          coords_flatten_2 = paddle.unsqueeze(coords_flatten, 1)
          relative_coords = (
              coords_flatten_1.clone() - coords_flatten_2.clone()
          )
  
          relative_coords = relative_coords.transpose(
              (1, 2, 0)
          )  # Wh*Ww, Wh*Ww, 2
          relative_coords[:, :, 0] += (
              window_size[0] - 1
          )  # shift to start from 0
          relative_coords[:, :, 1] += window_size[1] - 1
          relative_coords[:, :, 0] *= 2 * window_size[1] - 1
          relative_position_index = paddle.zeros(
              shape=(window_size[0] * window_size[1] + 1,) * 2,
              dtype=relative_coords.dtype,
          )
          relative_position_index[1:, 1:] = relative_coords.sum(
              -1
          )  # Wh*Ww, Wh*Ww
          relative_position_index[0, 0:] = (
              self.num_relative_distance - 3
          )
          relative_position_index[0:, 0] = (
              self.num_relative_distance - 2
          )
          relative_position_index[0, 0] = self.num_relative_distance - 1
  
          self.register_buffer(
              "relative_position_index", relative_position_index
          )
      else:
          self.window_size = None
          self.relative_position_bias_table = None
          self.relative_position_index = None
  
      self.attn_drop = nn.Dropout(attn_drop)
      self.proj = nn.Linear(dim, dim)
      self.proj_drop = nn.Dropout(proj_drop)
  
imports: import os
  import math
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle.nn.initializer import TruncatedNormal, Constant, Normal
  from paddleseg.cvlibs import manager
  from paddleseg.utils import utils, logger
  from paddleseg.models.backbones.transformer_utils import to_2tuple, DropPath, Identity
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, qk_scale, attn_drop, proj_drop, window_size)