code: def __init__(
      self,
      d_hid,
      d_ff,
      d_positional,
      relu_dropout=0.1,
      residual_dropout=0.1,
  ):
      super().__init__()
      self.d_content = d_hid - d_positional
      self.w_1c = nn.Linear(self.d_content, d_ff // 2)
      self.w_1p = nn.Linear(d_positional, d_ff // 2)
      self.w_2c = nn.Linear(d_ff // 2, self.d_content)
      self.w_2p = nn.Linear(d_ff // 2, d_positional)
      self.layer_norm = LayerNormalization(d_hid)
      self.relu_dropout = FeatureDropout(relu_dropout)
      self.residual_dropout = FeatureDropout(residual_dropout)
      self.relu = nn.ReLU()
  
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 1 to create a new PartitionedPositionwiseFeedForward object from inherited base classes: nn with example: obj = PartitionedPositionwiseFeedForward(d_hid, d_ff, d_positional, relu_dropout, residual_dropout)