utils/gluon/utils/resnetv1.py [216:226]:
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        self.norm_kwargs = norm_kwargs

        with self.name_scope():
            self.conv1 = gluon.nn.HybridSequential()
            if not deep_stem:
                self.conv1.add(gluon.nn.Conv2D(channels=int(k*64), kernel_size=7, padding=3, strides=2,
                                         use_bias=False, prefix='conv1_'))
                self.conv1.add(gluon.nn.BatchNorm(prefix='bn1_',
                                         **({} if norm_kwargs is None else norm_kwargs)))
                self.conv1.add(gluon.nn.Activation('relu'))
            else:
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utils/gluon/utils/resnetv2.py [135:145]:
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        self.norm_kwargs = norm_kwargs

        with self.name_scope():
            self.conv1 = gluon.nn.HybridSequential()
            if not deep_stem:
                self.conv1.add(gluon.nn.Conv2D(channels=int(k*64), kernel_size=7, padding=3, strides=2,
                                         use_bias=False, prefix='conv1_'))
                self.conv1.add(gluon.nn.BatchNorm(prefix='bn1_',
                                         **({} if norm_kwargs is None else norm_kwargs)))
                self.conv1.add(gluon.nn.Activation('relu'))
            else:
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