class_docstr: The UAFM with channel attention, which uses mean 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.
code: def __init__(
      self, x_ch, y_ch, out_ch, ksize=3, resize_mode="bilinear"
  ):
      super().__init__(x_ch, y_ch, out_ch, ksize, resize_mode)
  
      self.conv_xy_atten = nn.Sequential(
          layers.ConvBNAct(
              2 * y_ch,
              y_ch // 2,
              kernel_size=1,
              bias_attr=False,
              act_type="leakyrelu",
          ),
          layers.ConvBN(
              y_ch // 2, y_ch, kernel_size=1, bias_attr=False
          ),
      )
  
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 1 to create a new UAFM_ChAtten_S object from inherited base classes: UAFM with example: obj = UAFM_ChAtten_S(x_ch, y_ch, out_ch, ksize, resize_mode)