code: def forward(self, x, batch_idxs):
      residual = x
      xc = x[:, : self.d_content]
      xp = x[:, self.d_content :]
  
      outputc = self.w_1c(xc)
      outputc = self.relu_dropout(self.relu(outputc), batch_idxs)
      outputc = self.w_2c(outputc)
  
      outputp = self.w_1p(xp)
      outputp = self.relu_dropout(self.relu(outputp), batch_idxs)
      outputp = self.w_2p(outputp)
  
      output = torch.cat([outputc, outputp], -1)
  
      output = self.residual_dropout(output, batch_idxs)
      return self.layer_norm(output + residual)
  
imports: import numpy
  import functools
  import sys
  import torch
  from torch.autograd import Variable
  import torch.nn
  import torch.nn.init
label_desc: Use image node_id 2 for calling the PartitionedPositionwiseFeedForward obj's underlying member method code with example usage: obj.forward(x, batch_idxs) and returns: self