class_docstr: AttentionModule Layer, which contains some depth-wise strip convolutions.
  
  Args:
      dim (int): Number of input channels.
      kernel_sizes (list[int], optional): The height or width of each strip convolution kernel. Default: [7, 11, 21].
class_docstr_tok: ["AttentionModule","Layer",",","which","contains","some","depth-wise","strip","convolutions",".","Args",":","dim","(","int",")",":","Number","of","input","channels",".","kernel_sizes","(","list","[","int","]",",","optional",")",":","The","height","or","width","of","each","strip","convolution","kernel",".","Default",":","[","7",",","11",",","21","]","."]
code: def forward(self, x):
      u = paddle.clone(x)
      attn = self.conv0(x)
  
      attns = [m(attn) for m in self.dwconvs]
  
      attn += sum(attns)
  
      attn = self.conv_out(attn)
  
      return attn * u
  
imports: import math
  import numpy
  import paddle
  import paddle.nn
  from paddle.nn import Conv2D
  from paddle.nn.initializer import Assign, Normal
  from paddleseg.cvlibs import manager
  from paddleseg.models.backbones.transformer_utils import DropPath, ones_, to_2tuple, zeros_
  from paddleseg.models.layers import SyncBatchNorm
  from paddleseg.utils import utils
label_desc: Use image node_id 2 for calling the AttentionModule obj's underlying member method code with example usage: obj.forward(x) and returns: unknown