code: def __init__(
      self,
      dim,
      num_heads=8,
      qkv_bias=False,
      attn_bias=False,
      attn_drop=0.0,
      proj_drop=0.0,
      use_rel_pos=False,
      rel_pos_zero_init=True,
      window_size=None,
      input_size=None,
      qk_scale=None,
      lr_factor=1.0,
  ):
      super().__init__()
      self.num_heads = num_heads
      self.head_dim = dim // num_heads
      self.scale = qk_scale or self.head_dim**-0.5
      self.use_rel_pos = use_rel_pos
      self.input_size = input_size
      self.rel_pos_zero_init = rel_pos_zero_init
      self.window_size = window_size
      self.lr_factor = lr_factor
  
      self.qkv = nn.Linear(
          dim,
          dim * 3,
          weight_attr=ParamAttr(learning_rate=lr_factor),
          bias_attr=ParamAttr(learning_rate=lr_factor)
          if attn_bias
          else 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
      self.proj = nn.Linear(
          dim,
          dim,
          weight_attr=ParamAttr(learning_rate=lr_factor),
          bias_attr=ParamAttr(learning_rate=lr_factor),
      )
      self.attn_drop = nn.Dropout(attn_drop)
      if window_size is None:
          self.window_size = self.input_size[0]
  
      self._init_weights()
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  import numpy
  import math
  from paddle import ParamAttr
  from paddle.regularizer import L2Decay
  from paddle.nn.initializer import Constant, TruncatedNormal
  from ppdet.modeling.shape_spec import ShapeSpec
  from ppdet.core.workspace import register, serializable
  from .transformer_utils import zeros_, DropPath, Identity, window_partition, window_unpartition
  from ..initializer import linear_init_
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, attn_bias, attn_drop, proj_drop, use_rel_pos, rel_pos_zero_init, window_size, input_size, qk_scale, lr_factor)