lib/models/conv.py [24:35]:
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        self.deconv5 = nn.ConvTranspose2d(n_filters, n_out, 4, 2, 1)

        if activation == 'relu':
            self.activation = F.relu
        elif activation == 'lrelu':
            self.activation = lambda x: F.leaky_relu(x, 0.2)
        else:
            raise ValueError()

        self.batchnorm = batchnorm

    def forward(self, z):
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lib/models/conv.py [117:129]:
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        self.deconv5 = nn.ConvTranspose2d(n_filters, n_out, 4, 2, 1)

        if activation == 'relu':
            self.activation = F.relu
        elif activation == 'lrelu':
            self.activation = lambda x: F.leaky_relu(x, 0.2)
        else:
            raise ValueError()

        self.batchnorm = batchnorm


    def forward(self, z):
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