class_docstr: ### Position-wise Feed Forward Layer $      ext{F\small{FW}}$
  
  This consists of two linear layers and an activation in the middle.
class_docstr_tok: ["#","#","#","Position-wise","Feed","Forward","Layer","$","ext","{","F\\small","{","FW","}","}","$","This","consists","of","two","linear","layers","and","an","activation","in","the","middle","."]
docstr: `h` are the embeddings of shape `[batch_size, seq_len, d_model]`
docstr_tok: ["`","h","`","are","the","embeddings","of","shape","`","[","batch_size",",","seq_len",",","d_model","]","`"]
code: def forward(self, h: torch.Tensor):
      """
      `h` are the embeddings of shape `[batch_size, seq_len, d_model]`
      """
  
      # Residual
      h_res = h
      # Pre-norm
      h = self.norm(h)
      # First linear layer
      h = self.lin1(h)
      # Activation
      h = self.act(h)
      # Second linear layer
      h = self.lin2(h)
  
      # Add the residual connection
      return h + h_res
  
imports: import math
  from typing import Set
  import torch
  from torch import nn
  from labml.logger import inspect
label_desc: Use image node_id 2 for calling the FeedForward obj's underlying member method code with example usage: obj.forward(h) and returns: unknown