code: def forward(self, x):
      B, C, H, W = x.shape
      if self.stride_conv is not None:
          x = self.stride_conv(x)
          H = H // 2
          W = W // 2
  
      q = (
          self.q(x)
          .flatten(2)
          .reshape([B, self.num_heads, -1, H * W])
          .transpose([0, 1, 3, 2])
      )
      k = (
          self.k(x)
          .flatten(2)
          .reshape([B, self.num_heads, -1, H * W])
          .transpose([0, 1, 2, 3])
      )
      v = self.v(x)
      v_local = self.v_local(v)
      v = (
          v.flatten(2)
          .reshape([B, self.num_heads, -1, H * W])
          .transpose([0, 1, 3, 2])
      )
  
      attn = (q @ k) * self.scale
      bias = paddle.gather(
          self.attention_biases_seg,
          self.attention_bias_idxs_seg.flatten(),
          axis=1,
      ).reshape(
          [
              self.attention_biases_seg.shape[0],
              self.attention_bias_idxs_seg.shape[0],
              self.attention_bias_idxs_seg.shape[1],
          ]
      )
  
      bias = F.interpolate(
          bias.unsqueeze(0), size=attn.shape[-2:], mode="bicubic"
      )
      attn = attn + bias
  
      attn = self.talking_head1(attn)
      attn = F.softmax(attn, axis=-1)
      attn = self.talking_head2(attn)
  
      x = attn @ v
  
      out = (
          x.transpose([0, 1, 3, 2]).reshape([B, self.dh, H, W])
          + v_local
      )
      if self.upsample is not None:
          out = self.upsample(out)
  
      out = self.proj(out)
      return out
  
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 2 for calling the Attention4D obj's underlying member method code with example usage: obj.forward(x) and returns: out