Make a context manager which enables back-propagation.

When you want to enable back-propagation in no_backprop_mode(), call this method. A Variable created in this context always has a computational graph unless overridden by deeper contexts. If you call this method outside of no_backprop_mode() context, it changes nothing.

In the following example, y has a computational graph and calling backward() on y will compute and accumulate the gradients of the variables in the graph, in this case only x.

>>> x = chainer.Variable(np.array([1,], 'f'))
>>> with chainer.no_backprop_mode():
...     with chainer.force_backprop_mode():
...         y = x + 1
>>> y.backward()
>>> x.grad
array([ 1.], dtype=float32)

See also

See no_backprop_mode() for details on disabled back-propagation mode.