models.py [227:241]:
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        if not FLAGS.cclass:
            label = None

        if stop_grad:
            for k, v in weights.items():
                if type(v) == dict:
                    v = v.copy()
                    weights[k] = v
                    for k_sub, v_sub in v.items():
                        v[k_sub] = tf.stop_gradient(v_sub)
                else:
                    weights[k] = tf.stop_gradient(v)

        # Make sure gradients are modified a bit
        inp = smart_conv_block(inp, weights, reuse, 'c1_pre', use_stride=False)
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models.py [491:505]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        if not FLAGS.cclass:
            label = None

        if stop_grad:
            for k, v in weights.items():
                if type(v) == dict:
                    v = v.copy()
                    weights[k] = v
                    for k_sub, v_sub in v.items():
                        v[k_sub] = tf.stop_gradient(v_sub)
                else:
                    weights[k] = tf.stop_gradient(v)

        # Make sure gradients are modified a bit
        inp = smart_conv_block(inp, weights, reuse, 'c1_pre', use_stride=False)
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