resnet.py [167:184]:
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    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(block(
                self.in_planes, planes, stride,
                batchnorm=self.batchnorm, nonlinearity=self.nonlinearity))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        out = self.conv1(x)
        if self.batchnorm:
            out = self.bn1(out)
        out = self.nonlinearity(out)
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
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resnet.py [207:224]:
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    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(block(
                self.in_planes, planes, stride,
                batchnorm=self.batchnorm, nonlinearity=self.nonlinearity))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        out = self.conv1(x)
        if self.batchnorm:
            out = self.bn1(out)
        out = self.nonlinearity(out)
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
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