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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def generate(self, x, **kwargs):
 """ Return a tensor that constructs adversarial examples for the given input. Generate uses tf.py_func in order to operate over tensors. :param x: A tensor with the inputs. :param kwargs: See `parse_params` """

    assert self.sess is not None, \
        'Cannot use `generate` when no `sess` was provided'
    self.parse_params(**kwargs)

    labels, nb_classes = self.get_or_guess_labels(x, kwargs)

    attack = CWL2(self.sess, self.model, self.batch_size, self.confidence,
                  'y_target' in kwargs, self.learning_rate,
                  self.binary_search_steps, self.max_iterations,
                  self.abort_early, self.initial_const, self.clip_min,
                  self.clip_max, nb_classes,
                  x.get_shape().as_list()[1:])

    def cw_wrap(x_val, y_val):
      return np.array(attack.attack(x_val, y_val), dtype=self.np_dtype)

    wrap = tf.py_func(cw_wrap, [x, labels], self.tf_dtype)
    wrap.set_shape(x.get_shape())

    return wrap