models.py [485:502]:
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        return weights

    def forward(self, inp, weights, reuse=False, scope='', stop_grad=False, label=None, stop_at_grad=False, stop_batch=False):
        weights = weights.copy()
        batch = tf.shape(inp)[0]

        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)
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models.py [572:590]:
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        return weights

    def forward(self, inp, weights, reuse=False, scope='', stop_grad=False, label=None, stop_at_grad=False, stop_batch=False):
        weights = weights.copy()
        batch = tf.shape(inp)[0]

        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)
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