scripts/models/wider_resnet.py [67:87]:
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                self.add_module(
                    "pool%d" % (mod_id + 2), nn.MaxPool2d(3, stride=2, padding=1)
                )
            self.add_module("mod%d" % (mod_id + 2), nn.Sequential(OrderedDict(blocks)))

        # Pooling and predictor
        self.bn_out = norm_act(in_channels)
        if classes != 0:
            self.classifier = nn.Sequential(
                OrderedDict(
                    [
                        ("avg_pool", GlobalAvgPool2d()),
                        ("fc", nn.Linear(in_channels, classes)),
                    ]
                )
            )

    def forward(self, img):
        out = self.mod1(img)
        out = self.mod2(self.pool2(out))
        out = self.mod3(self.pool3(out))
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scripts/models/wider_resnet.py [184:204]:
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                self.add_module(
                    "pool%d" % (mod_id + 2), nn.MaxPool2d(3, stride=2, padding=1)
                )
            self.add_module("mod%d" % (mod_id + 2), nn.Sequential(OrderedDict(blocks)))

        # Pooling and predictor
        self.bn_out = norm_act(in_channels)
        if classes != 0:
            self.classifier = nn.Sequential(
                OrderedDict(
                    [
                        ("avg_pool", GlobalAvgPool2d()),
                        ("fc", nn.Linear(in_channels, classes)),
                    ]
                )
            )

    def forward(self, img):
        out = self.mod1(img)
        out = self.mod2(self.pool2(out))
        out = self.mod3(self.pool3(out))
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