class_docstr: Model with Conv2d layers, all with bias, some in a Sequential and some following, and then a Pool2d
  and a Flatten module followed by a Linear layer.
  Activation functions and Pool2ds in between each layer also.
  Used to test pruned Conv2d-Pool2d-Flatten-Linear fusion.
class_docstr_tok: ["Model","with","Conv2d","layers",",","all","with","bias",",","some","in","a","Sequential","and","some","following",",","and","then","a","Pool2d","and","a","Flatten","module","followed","by","a","Linear","layer",".","Activation","functions","and","Pool2ds","in","between","each","layer","also",".","Used","to","test","pruned","Conv2d-Pool2d-Flatten-Linear","fusion","."]
code: def __init__(self):
      super().__init__()
      self.seq = nn.Sequential(
          nn.Conv2d(1, 3, kernel_size=3, padding=1, bias=True),
          nn.MaxPool2d(kernel_size=2, stride=2, padding=1),
          nn.ReLU(),
          nn.Conv2d(3, 5, kernel_size=3, padding=1, bias=True),
          nn.Tanh(),
          nn.AvgPool2d(kernel_size=2, stride=2, padding=1),
      )
      self.conv2d1 = nn.Conv2d(
          5, 7, kernel_size=3, padding=1, bias=True
      )
      self.af1 = nn.ReLU()
      self.conv2d2 = nn.Conv2d(
          7, 11, kernel_size=3, padding=1, bias=True
      )
      self.avg_pool = nn.AdaptiveAvgPool2d((2, 2))
      self.flatten = nn.Flatten()
      self.fc = nn.Linear(44, 13, 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 Conv2dPoolFlatten object from inherited base classes: nn with example: obj = Conv2dPoolFlatten()