class_docstr: Model with Conv2d layers, all with bias, some in a Sequential and some following, and then a Pool2d
  and a functional Flatten 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","functional","Flatten","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 forward(self, x):
      x = self.seq(x)
      x = self.conv2d1(x)
      x = F.max_pool2d(x, kernel_size=2, stride=2, padding=1)
      x = self.af1(x)
      x = self.conv2d2(x)
      x = self.avg_pool(x)
      x = torch.flatten(x, 1)  # test functional flatten
      x = self.fc(x)
      return x
  
imports: from torch.ao.pruning import BaseSparsifier
  import torch
  import torch.nn.functional
  from torch import nn
label_desc: Use image node_id 2 for calling the Conv2dPoolFlattenFunctional obj's underlying member method code with example usage: obj.forward(x) and returns: x