code: def forward(self, x):
      x0 = self.linear0(x[0])
      x1 = self.linear1(x[1])
      bs = x1.shape[0]
      if self.dropout_input > 0:
          x0 = F.dropout(
              x0, p=self.dropout_input, training=self.training
          )
          x1 = F.dropout(
              x1, p=self.dropout_input, training=self.training
          )
      x0_chunks = paddle.split(x0, self.chunks, -1)
      x1_chunks = paddle.split(x1, self.chunks, -1)
      zs = []
      for x0_c, x1_c, m0, m1 in zip(
          x0_chunks, x1_chunks, self.merge_linears0, self.merge_linears1
      ):
          m = m0(x0_c) * m1(x1_c)  # bs x split_size*rank
          m = m.reshape([bs, self.rank, -1])
          z = paddle.sum(m, 1)
          if self.pos_norm == "before_cat":
              z = paddle.sqrt(F.relu(z)) - paddle.sqrt(F.relu(-z))
              z = F.normalize(z)
          zs.append(z)
      z = paddle.concat(zs, 1)
      if self.pos_norm == "after_cat":
          z = paddle.sqrt(F.relu(z)) - paddle.sqrt(F.relu(-z))
          z = F.normalize(z)
  
      if self.dropout_pre_lin > 0:
          z = F.dropout(
              z, p=self.dropout_pre_lin, training=self.training
          )
      z = self.linear_out(z)
      if self.dropout_output > 0:
          z = F.dropout(
              z, p=self.dropout_output, training=self.training
          )
      return z
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import paddle
  from paddle import nn
  import paddle.nn.functional
  from paddle import ParamAttr
label_desc: Use image node_id 2 for calling the Block obj's underlying member method code with example usage: obj.forward(x) and returns: z