code: def __init__(
      self,
      dim=384,
      key_dim=32,
      num_heads=8,
      attn_ratio=4,
      resolution=7,
      act_layer=nn.ReLU,
      stride=None,
  ):
      super().__init__()
      self.num_heads = num_heads
      self.scale = key_dim**-0.5
      self.key_dim = key_dim
      self.nh_kd = nh_kd = key_dim * num_heads
  
      if stride is not None:
          self.resolution = math.ceil(resolution / stride)
          self.stride_conv = nn.Sequential(
              nn.Conv2D(
                  dim,
                  dim,
                  kernel_size=3,
                  stride=stride,
                  padding=1,
                  groups=dim,
              ),
              nn.BatchNorm2D(dim),
          )
          self.upsample = nn.Upsample(
              scale_factor=stride, mode="bilinear"
          )
      else:
          self.resolution = resolution
          self.stride_conv = None
          self.upsample = None
  
      self.N = self.resolution**2
      self.N2 = self.N
      self.d = int(attn_ratio * key_dim)
      self.dh = int(attn_ratio * key_dim) * num_heads
      self.attn_ratio = attn_ratio
      h = self.dh + nh_kd * 2
      self.q = nn.Sequential(
          nn.Conv2D(dim, self.num_heads * self.key_dim, 1),
          nn.BatchNorm2D(self.num_heads * self.key_dim),
      )
      self.k = nn.Sequential(
          nn.Conv2D(dim, self.num_heads * self.key_dim, 1),
          nn.BatchNorm2D(self.num_heads * self.key_dim),
      )
      self.v = nn.Sequential(
          nn.Conv2D(dim, self.num_heads * self.d, 1),
          nn.BatchNorm2D(self.num_heads * self.d),
      )
      self.v_local = nn.Sequential(
          nn.Conv2D(
              self.num_heads * self.d,
              self.num_heads * self.d,
              kernel_size=3,
              stride=1,
              padding=1,
              groups=self.num_heads * self.d,
          ),
          nn.BatchNorm2D(self.num_heads * self.d),
      )
      self.talking_head1 = nn.Conv2D(
          self.num_heads,
          self.num_heads,
          kernel_size=1,
          stride=1,
          padding=0,
      )
      self.talking_head2 = nn.Conv2D(
          self.num_heads,
          self.num_heads,
          kernel_size=1,
          stride=1,
          padding=0,
      )
  
      self.proj = nn.Sequential(
          act_layer(),
          nn.Conv2D(self.dh, dim, 1),
          nn.BatchNorm2D(dim),
      )
  
      points = list(
          itertools.product(
              range(self.resolution), range(self.resolution)
          )
      )
      N = len(points)
      attention_offsets = {}
      idxs = []
      for p1 in points:
          for p2 in points:
              offset = (abs(p1[0] - p2[0]), abs(p1[1] - p2[1]))
              if offset not in attention_offsets:
                  attention_offsets[offset] = len(attention_offsets)
              idxs.append(attention_offsets[offset])
      self.register_buffer(
          "attention_biases", paddle.zeros([num_heads, 49])
      )
  
      self.register_buffer(
          "attention_bias_idxs",
          paddle.ones([49, 49], dtype=paddle.int64),
      )
  
      self.attention_biases_seg = self.create_parameter(
          shape=[num_heads, len(attention_offsets)],
          default_initializer=zeros_,
      )
      self.register_buffer(
          "attention_bias_idxs_seg",
          paddle.to_tensor(idxs).reshape([N, N]),
      )
  
imports: import math
  import itertools
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.utils import utils
  from paddleseg.cvlibs import manager, param_init
  from paddleseg.models.backbones.transformer_utils import *
label_desc: Use image node_id 1 to create a new Attention4D object from inherited base classes: nn with example: obj = Attention4D(dim, key_dim, num_heads, attn_ratio, resolution, act_layer, stride)