code: def feed_forward(self, input_data, prediction=False):
          
  
          activations = []
  
          for task in self.tasks:
              activations_task = task.feed_forward(input_data, prediction)
              activations.append(activations_task)
  
          return activations
docs: Call ``feed_forward`` for each task and combine the activations.
  
          Passes ``input_data`` to all tasks and returns the activations
          as a list.
      
          **Parameters:**
  
          input_data : ``GPUArray``
              Inpute data to compute activations for.
  
          prediction : bool, optional
              Whether to use prediction model. Only relevant when using
              dropout. If true, then weights are multiplied by
              1 - dropout if the layer uses dropout.
  
          **Returns:**
          
          activations : list of ``GPUArray``
              The activations of the output units, one element for each task.