Since batchnorm layer gathers statistics during the training step and reuse them later during inference, we have to define a new batchnorm layer every time it is used. The question is if this also applies to maxpooling or is it enough to define it once and use multiple times
if we take the follwing script as example is it enough to only use max_pooling1 layer for both usages
thnks
self.conv1 = nn.Conv2d(out_channel_4, out_channel_5, kernel_size=(9, 9), bias=True, stride=1, padding='same')
self.batch_Norm1 = nn.BatchNorm2d(out_channel_3, momentum=momentum)
self.conv2 = nn.Conv2d(out_channel_3, out_channel_4, kernel_size=(9, 9), bias=True, stride=1, padding='same')
self.batch_Norm2 = nn.BatchNorm2d(out_channel_4, momentum=momentum)
self.max_pooling1 = nn.MaxPool2d(2, stride=2)
self.max_pooling2 = nn.MaxPool2d(2, stride=2)

    def forward(self, x1):
        # block 1.1
        x1 = self.conv1(x1)
        x1 = self.batch_Norm1(x1)
        x1 = F.relu(x1)
        x1 = self.max_pooling1(x1)
        x1 = self.conv2(x1)
        x1 = self.batch_Norm2(x1)
        x1 = F.relu(x1)
        x1 = self.max_pooling2(x1)