class_docstr: Two-layer feed-forward module.
  
  Args:
      d_in (int): The dimension of the input for feedforward
          network model.
      d_hid (int): The dimension of the feedforward
          network model.
      dropout (float): Dropout layer on feedforward output.
      act_cfg (dict): Activation cfg for feedforward module.
code: def __init__(
      self, d_in, d_hid, dropout=0.1, act_cfg=dict(type="Relu")
  ):
      super().__init__()
      self.w_1 = nn.Linear(d_in, d_hid)
      self.w_2 = nn.Linear(d_hid, d_in)
      self.act = MODELS.build(act_cfg)
      self.dropout = nn.Dropout(dropout)
  
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from mmocr.registry import MODELS
label_desc: Use image node_id 1 to create a new PositionwiseFeedForward object from inherited base classes: nn with example: obj = PositionwiseFeedForward(d_in, d_hid, dropout, act_cfg)