 ) -> [Episode] {
     var observationNumpy = env.reset()
 
-    let context = Context(learningPhase: .inference)
     var episodes: [Episode] = []
 
     // We build up a batch of observations and actions.

        while true {
            let observationPython = Tensor<Double>(numpy: observationNumpy).unwrapped()
            let actionProbabilities =
              softmax(net.applied(to: Tensor(observationPython).reshaped(to: [1, 4]), in: context))
            let actionProbabilitiesPython = actionProbabilities[0].makeNumpyArray()
            let len = Python.len(actionProbabilitiesPython)
            assert(actionCount == Int(Python.len(actionProbabilitiesPython)))

    }

    @differentiable
    func applied(to input: Tensor<Float>) -> Tensor<Float> {
        return input.sequenced(through: l1, l2)
    }
}

