class_docstr: Model with only Linear layers, some with bias, some in a Sequential and some following.
  Activation functions modules in between each Linear in the Sequential, and functional
  activationals are called in between each outside layer.
  Used to test pruned Linear(Bias)-Activation-Linear fusion.
class_docstr_tok: ["Model","with","only","Linear","layers",",","some","with","bias",",","some","in","a","Sequential","and","some","following",".","Activation","functions","modules","in","between","each","Linear","in","the","Sequential",",","and","functional","activationals","are","called","in","between","each","outside","layer",".","Used","to","test","pruned","Linear","(","Bias",")","-Activation-Linear","fusion","."]
code: def forward(self, x):
      x = self.seq(x)
      x = self.linear1(x)
      x = F.relu(x)
      x = self.linear2(x)
      x = F.relu(x)
      x = self.linear3(x)
      x = F.relu(x)
      return x
  
imports: from torch.ao.pruning import BaseSparsifier
  import torch
  import torch.nn.functional
  from torch import nn
label_desc: Use image node_id 2 for calling the LinearActivationFunctional obj's underlying member method code with example usage: obj.forward(x) and returns: x