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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 backward(self, out_grads=None):
 """Run backward on all devices. A backward should be called after a call to the forward function. Backward cannot be called unless ``self.for_training`` is ``True``. Parameters out_grads : NDArray or list of NDArray, optional Gradient on the outputs to be propagated back. This parameter is only needed when bind is called on outputs that are not a loss function. """

        assert self.for_training, 're-bind with for_training=True to run backward'
        if out_grads is None:
            out_grads = []

        for i, (exec_, islice) in enumerate(zip(self.execs, self.slices)):
            out_grads_slice = []
            for grad, axis in zip(out_grads, self.output_layouts):
                if axis >= 0:
                    # pylint: disable=no-member
                    og_my_slice = nd.slice_axis(grad, axis=axis, begin=islice.start,
                                                end=islice.stop)
                    out_grads_slice.append(og_my_slice.as_in_context(self.contexts[i]))
                    # pylint: enable=no-member
                else:
                    out_grads_slice.append(grad.copyto(self.contexts[i]))
            exec_.backward(out_grads=out_grads_slice)