class_docstr: Model with only Conv2d layers, all with bias, some in a Sequential and some following.
  Activation function modules in between each layer, Pool2d modules in between each layer.
  Used to test pruned Conv2d-Pool2d-Conv2d fusion.
class_docstr_tok: ["Model","with","only","Conv2d","layers",",","all","with","bias",",","some","in","a","Sequential","and","some","following",".","Activation","function","modules","in","between","each","layer",",","Pool2d","modules","in","between","each","layer",".","Used","to","test","pruned","Conv2d-Pool2d-Conv2d","fusion","."]
code: def forward(self, x):
      x = self.seq(x)
      x = self.conv2d1(x)
      x = self.maxpool(x)
      x = self.af1(x)
      x = self.conv2d2(x)
      x = F.avg_pool2d(x, kernel_size=2, stride=2, padding=1)
      x = F.relu(x)
      x = self.conv2d3(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 Conv2dPool obj's underlying member method code with example usage: obj.forward(x) and returns: x