old_contents: import gpytorch
  import torch
  from torch.autograd import Variable
  from .kernel import Kernel
  from gpytorch.lazy import ToeplitzLazyVariable, KroneckerProductLazyVariable, NonLazyVariable
  
  
  class GridInterpolationKernel(Kernel):
      def __init__(self, base_kernel_module, grid_size, grid, inducing_points):
          super(GridInterpolationKernel, self).__init__()
          self.base_kernel_module = base_kernel_module
          self.grid_size = grid_size
          self.register_buffer('grid', grid)
          self.register_buffer('_inducing_points', inducing_points)
  
      def train(self, mode=True):
          if hasattr(self, '_cached_kernel_mat'):
              del self._cached_kernel_mat
          return super(GridInterpolationKernel, self).train(mode)
  
      def forward(self, x1, x2, **kwargs):
          if not torch.equal(x1.data, self._inducing_points) or \
                  not torch.equal(x1.data, self._inducing_points):
              raise RuntimeError('The kernel should only receive the inducing points as input')
  
          if not self.training and hasattr(self, '_cached_kernel_mat'):
              return self._cached_kernel_mat
          else:
              d = x1.size(1)
              grid_var = Variable(self.grid)
              if d > 1:
                  k_UUs = Variable(x1.data.new(d, self.grid_size).zero_())
                  for i in range(d):
                      k_UUs[i] = self.base_kernel_module(grid_var[i, 0], grid_var[i], **kwargs).squeeze()
                  K_XX = KroneckerProductLazyVariable(k_UUs)
  
              else:
                  if gpytorch.functions.use_toeplitz:
                      k_UU = self.base_kernel_module(grid_var[0, 0], grid_var[0], **kwargs).squeeze()
                      K_XX = ToeplitzLazyVariable(k_UU)
                  else:
                      for i in range(100):
                          k_UU = self.base_kernel_module(grid_var[0], grid_var[0], **kwargs).squeeze()
                      K_XX = NonLazyVariable(k_UU)
  
              if not self.training:
                  self._cached_kernel_mat = K_XX
              return K_XX
  
new_contents: import gpytorch
  import torch
  from torch.autograd import Variable
  from .kernel import Kernel
  from gpytorch.lazy import ToeplitzLazyVariable, KroneckerProductLazyVariable, NonLazyVariable
  
  
  class GridInterpolationKernel(Kernel):
      def __init__(self, base_kernel_module, grid_size, grid, inducing_points):
          super(GridInterpolationKernel, self).__init__()
          self.base_kernel_module = base_kernel_module
          self.grid_size = grid_size
          self.register_buffer('grid', grid)
          self.register_buffer('_inducing_points', inducing_points)
  
      def train(self, mode=True):
          if hasattr(self, '_cached_kernel_mat'):
              del self._cached_kernel_mat
          return super(GridInterpolationKernel, self).train(mode)
  
      def forward(self, x1, x2, **kwargs):
          if not torch.equal(x1.data, self._inducing_points) or \
                  not torch.equal(x1.data, self._inducing_points):
              raise RuntimeError('The kernel should only receive the inducing points as input')
  
          if not self.training and hasattr(self, '_cached_kernel_mat'):
              return self._cached_kernel_mat
          else:
              d = x1.size(1)
              grid_var = Variable(self.grid)
              if d > 1:
                  k_UUs = Variable(x1.data.new(d, self.grid_size).zero_())
                  for i in range(d):
                      k_UUs[i] = self.base_kernel_module(grid_var[i, 0], grid_var[i], **kwargs).squeeze()
                  K_XX = KroneckerProductLazyVariable(k_UUs)
  
              else:
                  if gpytorch.functions.use_toeplitz:
                      k_UU = self.base_kernel_module(grid_var[0, 0], grid_var[0], **kwargs).squeeze()
                      K_XX = ToeplitzLazyVariable(k_UU)
                  else:
                      k_UU = self.base_kernel_module(grid_var[0], grid_var[0], **kwargs).squeeze()
                      K_XX = NonLazyVariable(k_UU)
  
              if not self.training:
                  self._cached_kernel_mat = K_XX
              return K_XX
  
subject: Remove old profiling code