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Calculates adversarial loss from generated adversarial samples.
nsl.lib.adv_regularizer( adv_neighbors, target_scores, model_fn, loss_fn )
adv_neighbors: dense (float32) tensor, with two possible shapes: (a) pointwise samples: batch_size x feat_len, or (b) sequence samples: batch_size x seq_len x feat_len
target_scores: target tensor used to compute loss.
model_fn: a method that has input tensor (same shape as adv_neighbors), is_train and reuse as input, returns predicted logits.
loss_fn: a loss function that has target and predction as input, and returns a float scalar
adv_loss: a scalar (float32) for adversarial loss.