class_docstr: The ExternalAttention implementation based on PaddlePaddle.
  Args:
      in_channels (int, optional): The input channels.
      inter_channels (int, optional): The channels of intermediate feature.
      out_channels (int, optional): The output channels.
      num_heads (int, optional): The num of heads in attention. Default: 8
      use_cross_kv (bool, optional): Wheter use cross_kv. Default: False
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docstr: Args:
      x (Tensor): The input tensor. 
      cross_k (Tensor, optional): The dims is (n*144, c_in, 1, 1)
      cross_v (Tensor, optional): The dims is (n*c_in, 144, 1, 1)
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code: def forward(self, x, cross_k=None, cross_v=None):
      """
      Args:
          x (Tensor): The input tensor.
          cross_k (Tensor, optional): The dims is (n*144, c_in, 1, 1)
          cross_v (Tensor, optional): The dims is (n*c_in, 144, 1, 1)
      """
      x = self.norm(x)
      if not self.use_cross_kv:
          x = F.conv2d(
              x,
              self.k,
              bias=None,
              stride=2 if not self.same_in_out_chs else 1,
              padding=0,
          )  # n,c_in,h,w -> n,c_inter,h,w
          x = self._act_dn(x)  # n,c_inter,h,w
          x = F.conv2d(
              x, self.v, bias=None, stride=1, padding=0
          )  # n,c_inter,h,w -> n,c_out,h,w
      else:
          assert (cross_k is not None) and (
              cross_v is not None
          ), "cross_k and cross_v should no be None when use_cross_kv"
          B = x.shape[0]
          assert (
              B > 0
          ), "The first dim of x ({}) should be greater than 0, please set input_shape for export.py".format(
              B
          )
          x = x.reshape([1, -1, 0, 0])  # n,c_in,h,w -> 1,n*c_in,h,w
          x = F.conv2d(
              x, cross_k, bias=None, stride=1, padding=0, groups=B
          )  # 1,n*c_in,h,w -> 1,n*144,h,w  (group=B)
          x = self._act_sn(x)
          x = F.conv2d(
              x, cross_v, bias=None, stride=1, padding=0, groups=B
          )  # 1,n*144,h,w -> 1, n*c_in,h,w  (group=B)
          x = x.reshape(
              [-1, self.in_channels, 0, 0]
          )  # 1, n*c_in,h,w -> n,c_in,h,w  (c_in = c_out)
      return x
  
imports: from functools import partial
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager, param_init
  from paddleseg.models import layers
  from paddleseg.utils import utils
  from paddleseg.models.backbones.transformer_utils import DropPath, Identity
  from paddleseg.cvlibs.param_init import constant_init, kaiming_normal_init, trunc_normal_init
label_desc: Use image node_id 5 for calling the ExternalAttention obj's underlying member method code with example usage: obj.forward(x, cross_k, cross_v) and returns: x