old_contents: from .random_variable import RandomVariable
  from torch.autograd import Variable
  from gpytorch.lazy import LazyVariable
  
  
  class GaussianRandomVariable(RandomVariable):
      def __init__(self, mean, covar):
          """
          Constructs a multivariate Gaussian random variable, based on mean and covariance
          Can be multivariate, or a batch of multivariate gaussians
  
          Passing a vector mean corresponds to a multivariate Gaussian
          Passing a matrix mean corresponds to a batch of multivariate Gaussians
  
          Params:
          - mean (Variable: vector n or matrix b x n) mean of Gaussian distribution
          - covar (Variable: matrix n x n or batch matrix b x n x n) covariance of Gaussian distribution
          """
          if not isinstance(mean, Variable) and not isinstance(mean, LazyVariable):
              raise RuntimeError('The mean of a GaussianRandomVariable must be a Variable')
  
          if not isinstance(covar, Variable) and not isinstance(covar, LazyVariable):
              raise RuntimeError('The covariance of a GaussianRandomVariable must be a Variable')
  
          print(mean.size(), covar.size())
          if not (mean.ndimension() == 1 or mean.ndimension() == 2):
              raise RuntimeError('mean should be a vector or a matrix (batch mode)')
  
          self._mean = mean
          self._covar = covar
  
      def covar(self):
          return self._covar
  
      def mean(self):
          return self._mean
  
      def representation(self):
          return self._mean, self._covar
  
      def sample(self, n_samples):
          raise NotImplementedError
  
      def var(self):
          return self._covar.diag()
  
      def __len__(self):
          if self._mean.ndimension() == 1:
              return 1
          return self._mean.size(0)
  
new_contents: from .random_variable import RandomVariable
  from torch.autograd import Variable
  from gpytorch.lazy import LazyVariable
  
  
  class GaussianRandomVariable(RandomVariable):
      def __init__(self, mean, covar):
          """
          Constructs a multivariate Gaussian random variable, based on mean and covariance
          Can be multivariate, or a batch of multivariate gaussians
  
          Passing a vector mean corresponds to a multivariate Gaussian
          Passing a matrix mean corresponds to a batch of multivariate Gaussians
  
          Params:
          - mean (Variable: vector n or matrix b x n) mean of Gaussian distribution
          - covar (Variable: matrix n x n or batch matrix b x n x n) covariance of Gaussian distribution
          """
          if not isinstance(mean, Variable) and not isinstance(mean, LazyVariable):
              raise RuntimeError('The mean of a GaussianRandomVariable must be a Variable')
  
          if not isinstance(covar, Variable) and not isinstance(covar, LazyVariable):
              raise RuntimeError('The covariance of a GaussianRandomVariable must be a Variable')
  
          if not (mean.ndimension() == 1 or mean.ndimension() == 2):
              raise RuntimeError('mean should be a vector or a matrix (batch mode)')
  
          self._mean = mean
          self._covar = covar
  
      def covar(self):
          return self._covar
  
      def mean(self):
          return self._mean
  
      def representation(self):
          return self._mean, self._covar
  
      def sample(self, n_samples):
          raise NotImplementedError
  
      def var(self):
          return self._covar.diag()
  
      def __len__(self):
          if self._mean.ndimension() == 1:
              return 1
          return self._mean.size(0)
  
subject: Remove print statement