class_docstr: Feature Fusion Module Implementation.
  This module fuses high-resolution feature and low-resolution feature.
  Args:
      high_in_channels (int): The channels of high-resolution feature (output of LearningToDownsample).
      low_in_channels (int): The channels of low-resolution feature (output of GlobalFeatureExtractor).
      out_channels (int): The output channels of this module.
      align_corners (bool): An argument of F.interpolate. It should be set to False when the output size of feature
          is even, e.g. 1024x512, otherwise it is True, e.g. 769x769.
code: def forward(self, high_res_input, low_res_input):
      low_res_input = F.interpolate(
          low_res_input,
          paddle.shape(high_res_input)[2:],
          mode="bilinear",
          align_corners=self.align_corners,
      )
      low_res_input = self.dwconv(low_res_input)
      low_res_input = self.conv_low_res(low_res_input)
      high_res_input = self.conv_high_res(high_res_input)
      x = high_res_input + low_res_input
  
      return F.relu(x)
  
imports: import paddle.nn
  import paddle.nn.functional
  import paddle
  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 FeatureFusionModule obj's underlying member method code with example usage: obj.forward(high_res_input, low_res_input) and returns: F