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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _gradient_penalty(self, real_samples, fake_samples, kwargs):
 """ Compute the norm of the gradients for each sample in a batch, and penalize anything on either side of unit norm """

        import torch
        from torch.autograd import Variable, grad

        real_samples = real_samples.view(fake_samples.shape)

        subset_size = real_samples.shape[0]

        real_samples = real_samples[:subset_size]
        fake_samples = fake_samples[:subset_size]

        alpha = torch.rand(subset_size)
        if self.use_cuda:
            alpha = alpha.cuda()
        alpha = alpha.view((-1,) + ((1,) * (real_samples.dim() - 1)))

        interpolates = alpha * real_samples + ((1 - alpha) * fake_samples)
        interpolates = Variable(interpolates, requires_grad=True)
        if self.use_cuda:
            interpolates = interpolates.cuda()

        d_output = self.critic(interpolates, **kwargs)

        grad_ouputs = torch.ones(d_output.size())
        if self.use_cuda:
            grad_ouputs = grad_ouputs.cuda()

        gradients = grad(
            outputs=d_output,
            inputs=interpolates,
            grad_outputs=grad_ouputs,
            create_graph=True,
            retain_graph=True,
            only_inputs=True)[0]
        return ((gradients.norm(2, dim=1) - 1) ** 2).mean() * 10