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 __init__(self):
      super().__init__()
      self.seq = nn.Sequential(
          nn.Linear(7, 5, bias=True),
          nn.ReLU(),
          nn.Linear(5, 6, bias=False),
          nn.ReLU(),
          nn.Linear(6, 4, bias=True),
      )
      self.linear1 = nn.Linear(4, 3, bias=True)
      self.linear2 = nn.Linear(3, 8, bias=False)
      self.linear3 = nn.Linear(8, 10, bias=False)
      self.act1 = nn.ReLU()
  
imports: from torch.ao.pruning import BaseSparsifier
  import torch
  import torch.nn.functional
  from torch import nn
label_desc: Use image node_id 1 to create a new LinearActivationFunctional object from inherited base classes: nn with example: obj = LinearActivationFunctional()