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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def feed_forward(self, input_data, prediction=False):
 """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. """


        activations = []

        for task in self.tasks:
            activations_task = task.feed_forward(input_data, prediction)
            activations.append(activations_task)

        return activations