class_docstr: The UAFM with channel attention, which uses mean and max values.
  Args:
      x_ch (int): The channel of x tensor, which is the low level feature.
      y_ch (int): The channel of y tensor, which is the high level feature.
      out_ch (int): The channel of output tensor.
      ksize (int, optional): The kernel size of the conv for x tensor. Default: 3.
      resize_mode (str, optional): The resize model in unsampling y tensor. Default: bilinear.
docstr: Args:
      x (Tensor): The low level feature.
      y (Tensor): The high level feature.
code: def fuse(self, x, y):
      """
      Args:
          x (Tensor): The low level feature.
          y (Tensor): The high level feature.
      """
      atten = helper.avg_max_reduce_hw([x, y], self.training)
      atten = F.sigmoid(self.conv_xy_atten(atten))
  
      out = x * atten + y * (1 - atten)
      out = self.conv_out(out)
      return out
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import ParamAttr
  from paddle.nn.initializer import Constant
  from paddleseg.models import layers
  from paddleseg.models.layers import tensor_fusion_helper
label_desc: Use image node_id 2 for calling the UAFM_ChAtten obj's underlying member method code with example usage: obj.fuse(x, y) and returns: out