class_docstr: A position-wise feed forward module.
  
  Projects to a higher-dimensional space before applying ReLU, then projects
  back.
class_docstr_tok: ["A","position-wise","feed","forward","module",".","Projects","to","a","higher-dimensional","space","before","applying","ReLU",",","then","projects","back","."]
code: def __init__(
      self, d_hid, d_ff, relu_dropout=0.1, residual_dropout=0.1
  ):
      super(PositionwiseFeedForward, self).__init__()
      self.w_1 = nn.Linear(d_hid, d_ff)
      self.w_2 = nn.Linear(d_ff, d_hid)
  
      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 PositionwiseFeedForward object from inherited base classes: nn with example: obj = PositionwiseFeedForward(d_hid, d_ff, relu_dropout, residual_dropout)