class_docstr: Model with only Conv2d layers, all with bias and some with padding > 0,
  some in a Sequential and some following. Activation function modules in between each layer.
  Used to test that bias is propagated correctly in the special case of
  pruned Conv2d-Bias-(Activation)Conv2d fusion, when the second Conv2d layer has padding > 0.
class_docstr_tok: ["Model","with","only","Conv2d","layers",",","all","with","bias","and","some","with","padding",">","0",",","some","in","a","Sequential","and","some","following",".","Activation","function","modules","in","between","each","layer",".","Used","to","test","that","bias","is","propagated","correctly","in","the","special","case","of","pruned","Conv2d-Bias-","(","Activation",")","Conv2d","fusion",",","when","the","second","Conv2d","layer","has","padding",">","0","."]
code: def __init__(self):
      super().__init__()
      self.seq = nn.Sequential(
          nn.Conv2d(1, 32, 3, 1, padding=1, bias=True),
          nn.ReLU(),
          nn.Conv2d(32, 32, 3, 1, bias=False),
          nn.ReLU(),
          nn.Conv2d(32, 32, 3, 1, padding=1, bias=True),
          nn.ReLU(),
          nn.Conv2d(32, 32, 3, 1, padding=1, bias=True),
          nn.ReLU(),
          nn.Conv2d(32, 64, 3, 1, bias=True),
          nn.Tanh(),
      )
      self.conv2d1 = nn.Conv2d(64, 48, 3, 1, padding=1, bias=True)
      self.act1 = nn.ReLU()
      self.conv2d2 = nn.Conv2d(48, 52, 3, 1, padding=1, bias=True)
      self.act2 = nn.Tanh()
  
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 Conv2dPadBias object from inherited base classes: nn with example: obj = Conv2dPadBias()