code: def forward(self, x):
      b, c, h, w = paddle.shape(x)
      proj_q = self.q_conv(x)
      proj_q_h = (
          proj_q.transpose([0, 3, 1, 2])
          .reshape([b * w, -1, h])
          .transpose([0, 2, 1])
      )
      proj_q_w = (
          proj_q.transpose([0, 2, 1, 3])
          .reshape([b * h, -1, w])
          .transpose([0, 2, 1])
      )
  
      proj_k = self.k_conv(x)
      proj_k_h = proj_k.transpose([0, 3, 1, 2]).reshape([b * w, -1, h])
      proj_k_w = proj_k.transpose([0, 2, 1, 3]).reshape([b * h, -1, w])
  
      proj_v = self.v_conv(x)
      proj_v_h = proj_v.transpose([0, 3, 1, 2]).reshape([b * w, -1, h])
      proj_v_w = proj_v.transpose([0, 2, 1, 3]).reshape([b * h, -1, w])
  
      energy_h = (
          (paddle.bmm(proj_q_h, proj_k_h) + self.Inf(b, h, w))
          .reshape([b, w, h, h])
          .transpose([0, 2, 1, 3])
      )
      energy_w = paddle.bmm(proj_q_w, proj_k_w).reshape([b, h, w, w])
      concate = self.softmax(
          paddle.concat([energy_h, energy_w], axis=3)
      )
  
      attn_h = (
          concate[:, :, :, 0:h]
          .transpose([0, 2, 1, 3])
          .reshape([b * w, h, h])
      )
      attn_w = concate[:, :, :, h : h + w].reshape([b * h, w, w])
      out_h = (
          paddle.bmm(proj_v_h, attn_h.transpose([0, 2, 1]))
          .reshape([b, w, -1, h])
          .transpose([0, 2, 3, 1])
      )
      out_w = (
          paddle.bmm(proj_v_w, attn_w.transpose([0, 2, 1]))
          .reshape([b, h, -1, w])
          .transpose([0, 2, 1, 3])
      )
      return self.gamma * (out_h + out_w) + x
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
  from paddleseg.models import layers
  from paddleseg.utils import utils
label_desc: Use image node_id 2 for calling the CrissCrossAttention obj's underlying member method code with example usage: obj.forward(x) and returns: unknown