How can i perform Global average pooling before the last fully connected layer

Deep Learning
1

class ConvNet(nn.Module):
def init (self):
super(ConvNet, self). init ()
self.layer1 = nn.Sequential(
nn.Conv1d(1, 16, kernel_size=5, stride=1 , padding=2),
nn.BatchNorm1d(16),
nn.ReLU(),
nn.MaxPool1d(kernel_size=2, stride=2))
self.layer2 = nn.Sequential(
nn.Conv1d(16, 32 ,kernel_size=5, stride=1, padding=2),
nn.BatchNorm1d(32),
nn.ReLU(),
nn.MaxPool1d(kernel_size=2, stride=2))

    self.drop_out = nn.Dropout()
    
    #self.fc1 = nn.Linear(32*25, 1000)
    self.fc2 = nn.Linear(64, 2)

this step is how the data flow through these layers in forward pass

def forward(self, x):
    
    
    out = self.layer1(x)
    out = self.layer2(out)
    #out = F.adaptive_avg_pool2d(x, (1, 1))
    out = F.avg_pool1d(out,1)
    #out = self.layer3(out)
    #out = out.reshape(-1, 12)
    out = self.drop_out(out)
    #out = self.fc1(out)
    
    out = self.fc2(out)
    return out