old_contents: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  
  import torch
  from torch.nn import ModuleList
  from copy import deepcopy
  from gpytorch.means import Mean
  
  
  class MultitaskMean(Mean):
      """
      Convenience :class:`gpytorch.means.Mean` implementation for defining a different mean for each task in a multitask
      model. Expects a list of `n_tasks` different mean functions, each of which is applied to the given data in
      :func:`~gpytorch.means.MultitaskMean.forward` and returned as an `n x t` matrix of means, one for each task.
      """
      def __init__(self, base_means, n_tasks):
          """
          Args:
              base_means (:obj:`list` or :obj:`gpytorch.means.Mean`): If a list, each mean is applied to the data.
                  If a single mean (or a list containing a single mean), that mean is copied `t` times.
              n_tasks (int): Number of tasks. If base_means is a list, this should equal its length.
          """
          super(MultitaskMean, self).__init__()
  
          if isinstance(base_means, Mean):
              base_means = [base_means]
  
          if not isinstance(base_means, list) or (len(base_means) != 1 and len(base_means) != n_tasks):
              raise RuntimeError("base_means should be a list of means of length either 1 or n_tasks")
  
          if len(base_means) == 1:
              base_means = base_means + deepcopy([base_means[i] for i in range(n_tasks - 1)])
  
          self.base_means = ModuleList(base_means)
          self.n_tasks = n_tasks
  
      def forward(self, input):
          """
          Evaluate each mean in self.base_means on the input data, and return as an `n x t` matrix of means.
          """
          return torch.cat([sub_mean(input).unsqueeze(-1) for sub_mean in self.base_means], dim=-1)
  
new_contents: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  
  import torch
  from torch.nn import ModuleList
  from copy import deepcopy
  from gpytorch.means import Mean
  
  
  class MultitaskMean(Mean):
      """
      Convenience :class:`gpytorch.means.Mean` implementation for defining a different mean for each task in a multitask
      model. Expects a list of `n_tasks` different mean functions, each of which is applied to the given data in
      :func:`~gpytorch.means.MultitaskMean.forward` and returned as an `n x t` matrix of means, one for each task.
      """
      def __init__(self, base_means, n_tasks):
          """
          Args:
              base_means (:obj:`list` or :obj:`gpytorch.means.Mean`): If a list, each mean is applied to the data.
                  If a single mean (or a list containing a single mean), that mean is copied `t` times.
              n_tasks (int): Number of tasks. If base_means is a list, this should equal its length.
          """
          super(MultitaskMean, self).__init__()
  
          if isinstance(base_means, Mean):
              base_means = [base_means]
  
          if not isinstance(base_means, list) or (len(base_means) != 1 and len(base_means) != n_tasks):
              raise RuntimeError("base_means should be a list of means of length either 1 or n_tasks")
  
          if len(base_means) == 1:
              base_means = base_means + [deepcopy(base_means[0]) for i in range(n_tasks - 1)]
  
          self.base_means = ModuleList(base_means)
          self.n_tasks = n_tasks
  
      def forward(self, input):
          """
          Evaluate each mean in self.base_means on the input data, and return as an `n x t` matrix of means.
          """
          return torch.cat([sub_mean(input).unsqueeze(-1) for sub_mean in self.base_means], dim=-1)
  
subject: Fix multitaskmean bug