class_docstr: Userguidedcolorization, see https://github.com/haoyuying/colorization-pytorch
  
  Args:
      use_tanh (bool): Whether to use tanh as final activation function.
      load_checkpoint (str): Pretrained checkpoint path.
class_docstr_tok: ["Userguidedcolorization",",","see","https",":","\/\/github.com\/haoyuying\/colorization-pytorch","Args",":","use_tanh","(","bool",")",":","Whether","to","use","tanh","as","final","activation","function",".","load_checkpoint","(","str",")",":","Pretrained","checkpoint","path","."]
code: def forward(
      self,
      input_A: paddle.Tensor,
      input_B: paddle.Tensor,
      mask_B: paddle.Tensor,
      real_b: paddle.Tensor = None,
      real_B_enc: paddle.Tensor = None,
  ) -> paddle.Tensor:
      conv1_2 = self.model1(
          paddle.concat([input_A, input_B, mask_B], axis=1)
      )
      conv2_2 = self.model2(conv1_2[:, :, ::2, ::2])
      conv3_3 = self.model3(conv2_2[:, :, ::2, ::2])
      conv4_3 = self.model4(conv3_3[:, :, ::2, ::2])
      conv5_3 = self.model5(conv4_3)
      conv6_3 = self.model6(conv5_3)
      conv7_3 = self.model7(conv6_3)
      conv8_up = self.model8up(conv7_3) + self.model3short8(conv3_3)
      conv8_3 = self.model8(conv8_up)
  
      if self.classification:
          out_class = self.model_class(conv8_3)
          conv9_up = self.model9up(
              conv8_3.detach()
          ) + self.model2short9(conv2_2.detach())
          conv9_3 = self.model9(conv9_up)
          conv10_up = self.model10up(conv9_3) + self.model1short10(
              conv1_2.detach()
          )
          conv10_2 = self.model10(conv10_up)
          out_reg = self.model_out(conv10_2)
      else:
          out_class = self.model_class(conv8_3.detach())
          conv9_up = self.model9up(conv8_3) + self.model2short9(conv2_2)
          conv9_3 = self.model9(conv9_up)
          conv10_up = self.model10up(conv9_3) + self.model1short10(
              conv1_2
          )
          conv10_2 = self.model10(conv10_up)
          out_reg = self.model_out(conv10_2)
  
      return out_class, out_reg
  
file_path: PaddleHub/modules/image/Image_editing/colorization/user_guided_colorization/module.py
imports: import os
  import paddle
  import paddle.nn
  from paddle.nn import Conv2D, Conv2DTranspose
  from paddlehub.module.module import moduleinfo
  import paddlehub.vision.transforms
  from paddlehub.module.cv_module import ImageColorizeModule
  from .data_feed import ColorizePreprocess
label_desc: Use image node_id 5 for calling the UserGuidedColorization obj's underlying member method code with example usage: obj.forward(input_A, input_B, mask_B, real_b, real_B_enc) and returns: out_class,  out_reg