class_docstr: The base of Unified Attention Fusion Module.
  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 check(self, x, y):
      assert x.ndim == 4 and y.ndim == 4
      x_h, x_w = x.shape[2:]
      y_h, y_w = y.shape[2:]
      assert x_h >= y_h and x_w >= y_w
  
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 obj's underlying member method code with example usage: obj.check(x, y) without return types