old_contents: import torch
  from .lazy_variable import LazyVariable
  
  
  class MatmulLazyVariable(LazyVariable):
      def __init__(self, lhs, rhs):
          super(MatmulLazyVariable, self).__init__(lhs, rhs)
          self.lhs = lhs
          self.rhs = rhs
  
      def _matmul_closure_factory(self, lhs, rhs):
          def closure(tensor):
              return torch.matmul(lhs, rhs).matmul(tensor)
  
          return closure
  
      def _derivative_quadratic_form_factory(self, lhs, rhs):
          def closure(left_factor, right_factor):
              left_grad = left_factor.transpose(-1, -2).matmul(right_factor.matmul(rhs.transpose(-1, -2)))
              right_grad = lhs.transpose(-1, -2).matmul(left_factor.transpose(-1, -2)).matmul(right_factor)
              return left_grad, right_grad
  
          return closure
  
      def is_batch(self):
          return self.lhs.ndimension() > 2
  
      def diag(self):
          return (self.lhs * self.rhs.t()).sum(1)
  
      def evaluate(self):
          return torch.matmul(self.lhs, self.rhs)
  
      def size(self):
          return torch.Size((self.lhs.size()[0], self.lhs.size()[0]))
          if self.is_batch():
              return torch.Size((self.lhs.size()[0], self.lhs.size()[1], self.lhs.size()[1]))
          else:
              return torch.Size((self.lhs.size()[0], self.lhs.size()[0]))
  
      def __getitem__(self, indices):
          if self.ndimension() == 2:
              index1, index2 = indices
              return MatmulLazyVariable(self.lhs[index1, :], self.rhs[:, index2])
          else:
              raise NotImplementedError('Batch __getitem__ not implemented yet')
  
new_contents: import torch
  from .lazy_variable import LazyVariable
  
  
  class MatmulLazyVariable(LazyVariable):
      def __init__(self, lhs, rhs):
          super(MatmulLazyVariable, self).__init__(lhs, rhs)
          self.lhs = lhs
          self.rhs = rhs
  
      def _matmul_closure_factory(self, lhs, rhs):
          def closure(tensor):
              return torch.matmul(lhs, rhs).matmul(tensor)
  
          return closure
  
      def _derivative_quadratic_form_factory(self, lhs, rhs):
          def closure(left_factor, right_factor):
              left_grad = left_factor.transpose(-1, -2).matmul(right_factor.matmul(rhs.transpose(-1, -2)))
              right_grad = lhs.transpose(-1, -2).matmul(left_factor.transpose(-1, -2)).matmul(right_factor)
              return left_grad, right_grad
  
          return closure
  
      def is_batch(self):
          return self.lhs.ndimension() > 2
  
      def diag(self):
          return (self.lhs * self.rhs.t()).sum(1)
  
      def evaluate(self):
          return torch.matmul(self.lhs, self.rhs)
  
      def size(self):
          if self.is_batch():
              return torch.Size((self.lhs.size()[0], self.lhs.size()[1], self.lhs.size()[1]))
          else:
              return torch.Size((self.lhs.size()[0], self.lhs.size()[0]))
  
      def __getitem__(self, indices):
          if self.ndimension() == 2:
              index1, index2 = indices
              return MatmulLazyVariable(self.lhs[index1, :], self.rhs[:, index2])
          else:
              raise NotImplementedError('Batch __getitem__ not implemented yet')
  
subject: Fix typo