torchnet/engine/engine.py [47:54]:
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            for sample in state['iterator']:
                state['sample'] = sample
                self.hook('on_sample', state)

                def closure():
                    loss, output = state['network'](state['sample'])
                    state['output'] = output
                    state['loss'] = loss
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torchnet/engine/engine.py [80:87]:
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        for sample in state['iterator']:
            state['sample'] = sample
            self.hook('on_sample', state)

            def closure():
                loss, output = state['network'](state['sample'])
                state['output'] = output
                state['loss'] = loss
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