Creates a context manager which temporarily disables backpropagation.

Within this context, no computational graph will be formed unless force_backprop_mode() is used.

Arrays resulting from operations enclosed with this context will be disconnected from the computational graph. Trying to perform backpropagation from such arrays would result in an error.

x = chainerx.array([4, 3], numpy.float32)

with chainerx.no_backprop_mode():
    y = 2 * x + 1

y.backward()  # ! error

Benefits of no_backprop_mode include reduced CPU overhead of building computational graphs, and reduced consumption of device memory that would be otherwise retained for backward propagation.