class_docstr: Convenience :class:`gpytorch.means.Mean` implementation for defining a different mean for each task in a multitask
  model. Expects a list of `num_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.
docstr: Evaluate each mean in self.base_means on the input data, and return as an `n x t` matrix of means.
docstr_tok: ["Evaluate","each","mean","in","self.base_means","on","the","input","data",",","and","return","as","an","`","n","x","t","`","matrix","of","means","."]
code: 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,
      )
  
imports: from copy import deepcopy
  import torch
  from torch.nn import ModuleList
  from .mean import Mean
label_desc: Use image node_id 2 for calling the MultitaskMean obj's underlying member method code with example usage: obj.forward(input) and returns: torch