class_docstr: Model with only Linear layers, alternating layers with biases,
  wrapped in a Sequential. Used to test pruned Linear-Bias-Linear fusion.
class_docstr_tok: ["Model","with","only","Linear","layers",",","alternating","layers","with","biases",",","wrapped","in","a","Sequential",".","Used","to","test","pruned","Linear-Bias-Linear","fusion","."]
code: def __init__(self):
      super().__init__()
      self.seq = nn.Sequential(
          nn.Linear(7, 5, bias=True),
          nn.Linear(5, 6, bias=False),
          nn.Linear(6, 3, bias=True),
          nn.Linear(3, 3, bias=True),
          nn.Linear(3, 10, bias=False),
      )
  
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 LinearBias object from inherited base classes: nn with example: obj = LinearBias()