code: def forward(self, x, qf_input=None):
      h, w = x.shape[-2:]
      paddingBottom = int(np.ceil(h / 8) * 8 - h)
      paddingRight = int(np.ceil(w / 8) * 8 - w)
      x = nn.functional.pad(
          x, (0, paddingRight, 0, paddingBottom), mode="reflect"
      )
  
      x1 = self.m_head(x)
      x2 = self.m_down1(x1)
      x3 = self.m_down2(x2)
      x4 = self.m_down3(x3)
      x = self.m_body_encoder(x4)
      qf = self.qf_pred(x)
      x = self.m_body_decoder(x)
      qf_embedding = (
          self.qf_embed(qf_input)
          if qf_input is not None
          else self.qf_embed(qf)
      )
      gamma_3 = self.to_gamma_3(qf_embedding)
      beta_3 = self.to_beta_3(qf_embedding)
  
      gamma_2 = self.to_gamma_2(qf_embedding)
      beta_2 = self.to_beta_2(qf_embedding)
  
      gamma_1 = self.to_gamma_1(qf_embedding)
      beta_1 = self.to_beta_1(qf_embedding)
  
      x = x + x4
      x = self.m_up3[0](x)
      for i in range(self.nb):
          x = self.m_up3[i + 1](x, gamma_3, beta_3)
  
      x = x + x3
  
      x = self.m_up2[0](x)
      for i in range(self.nb):
          x = self.m_up2[i + 1](x, gamma_2, beta_2)
      x = x + x2
  
      x = self.m_up1[0](x)
      for i in range(self.nb):
          x = self.m_up1[i + 1](x, gamma_1, beta_1)
  
      x = x + x1
      x = self.m_tail(x)
      x = x[..., :h, :w]
  
      return x, qf
  
imports: from collections import OrderedDict
  import numpy
  import paddle.nn
label_desc: Use image node_id 2 for calling the FBCNN obj's underlying member method code with example usage: obj.forward(x, qf_input) and returns: x,  qf