class_docstr: The Expectation-Maximization Attention Unit (EMAU).
  
  Arguments:
      c (int): The input and output channel number.
      k (int): The number of the bases.
      stage_num (int): The iteration number for EM.
      momentum (float): The parameter for updating bases.
class_docstr_tok: ["The","Expectation-Maximization","Attention","Unit","(","EMAU",")",".","Arguments",":","c","(","int",")",":","The","input","and","output","channel","number",".","k","(","int",")",":","The","number","of","the","bases",".","stage_num","(","int",")",":","The","iteration","number","for","EM",".","momentum","(","float",")",":","The","parameter","for","updating","bases","."]
code: def forward(self, x):
      x_shape = paddle.shape(x)
      x = x.flatten(2)
      mu = paddle.tile(self.mu, [x_shape[0], 1, 1])
  
      with paddle.no_grad():
          for i in range(self.stage_num):
              x_t = paddle.transpose(x, [0, 2, 1])
              z = paddle.bmm(x_t, mu)
              z = F.softmax(z, axis=2)
              z_ = F.normalize(z, axis=1, p=1)
              mu = paddle.bmm(x, z_)
              mu = F.normalize(mu, axis=1, p=2)
  
      z_t = paddle.transpose(z, [0, 2, 1])
      x = paddle.matmul(mu, z_t)
      x = paddle.reshape(x, [0, self.c, x_shape[2], x_shape[3]])
  
      if self.training:
          mu = paddle.mean(mu, 0, keepdim=True)
          mu = F.normalize(mu, axis=1, p=2)
          mu = self.mu * (1 - self.momentum) + mu * self.momentum
          if paddle.distributed.get_world_size() > 1:
              paddle.distributed.all_reduce(mu)
              mu /= paddle.distributed.get_world_size()
          self.mu = mu
  
      return x
  
file_path: PaddleSeg/paddleseg/models/emanet.py
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.models import layers
  from paddleseg.cvlibs import manager
  from paddleseg.utils import utils
label_desc: Use image node_id 2 for calling the EMAU obj's underlying member method code with example usage: obj.forward(x) and returns: x