class_docstr: Model with only Conv2d layers, some with bias, some in a Sequential and some following.
  Activation function modules in between each Sequential layer, functional activations called
  in-between each outside layer.
  Used to test pruned Conv2d-Bias-Activation-Conv2d fusion.
class_docstr_tok: ["Model","with","only","Conv2d","layers",",","some","with","bias",",","some","in","a","Sequential","and","some","following",".","Activation","function","modules","in","between","each","Sequential","layer",",","functional","activations","called","in-between","each","outside","layer",".","Used","to","test","pruned","Conv2d-Bias-Activation-Conv2d","fusion","."]
code: def __init__(self):
      super().__init__()
      self.seq = nn.Sequential(
          nn.Conv2d(1, 32, 3, 1, bias=True),
          nn.ReLU(),
          nn.Conv2d(32, 64, 3, 1, bias=True),
          nn.Tanh(),
          nn.Conv2d(64, 64, 3, 1, bias=False),
          nn.ReLU(),
      )
      self.conv2d1 = nn.Conv2d(64, 48, 3, 1, bias=False)
      self.conv2d2 = nn.Conv2d(48, 52, 3, 1, bias=True)
  
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 Conv2dActivation object from inherited base classes: nn with example: obj = Conv2dActivation()