Generate symbolic graph for adversarial examples and return.

    :param x: The model's symbolic inputs.
    :param kwargs: See `parse_params`

def generate(self, x, **kwargs):
    """
    Generate symbolic graph for adversarial examples and return.

    :param x: The model's symbolic inputs.
    :param kwargs: See `parse_params`
    """
    # Parse and save attack-specific parameters
    assert self.parse_params(**kwargs)

    asserts = []

    # If a data range was specified, check that the input was in that range
    if self.clip_min is not None:
      asserts.append(utils_tf.assert_greater_equal(x,
                                                   tf.cast(self.clip_min,
                                                           x.dtype)))

    if self.clip_max is not None:
      asserts.append(utils_tf.assert_less_equal(x,
                                                tf.cast(self.clip_max,
                                                        x.dtype)))

    # Initialize loop variables
    if self.rand_init:
      eta = tf.random_uniform(tf.shape(x),
                              tf.cast(-self.rand_minmax, x.dtype),
                              tf.cast(self.rand_minmax, x.dtype),
                              dtype=x.dtype)
    else:
      eta = tf.zeros(tf.shape(x))

    # Clip eta
    eta = clip_eta(eta, self.ord, self.eps)
    adv_x = x + eta
    if self.clip_min is not None or self.clip_max is not None:
      adv_x = utils_tf.clip_by_value(adv_x, self.clip_min, self.clip_max)

    if self.y_target is not None:
      y = self.y_target
      targeted = True
    elif self.y is not None:
      y = self.y
      targeted = False
    else:
      model_preds = self.model.get_probs(x)
      preds_max = tf.reduce_max(model_preds, 1, keepdims=True)
      y = tf.to_float(tf.equal(model_preds, preds_max))
      y = tf.stop_gradient(y)
      targeted = False
      del model_preds

    y_kwarg = 'y_target' if targeted else 'y'
    fgm_params = {
        'eps': self.eps_iter,
        y_kwarg: y,
        'ord': self.ord,
        'clip_min': self.clip_min,
        'clip_max': self.clip_max
    }
    if self.ord == 1:
      raise NotImplementedError("It's not clear that FGM is a good inner loop"
                                " step for PGD when ord=1, because ord=1 FGM "
                                " changes only one pixel at a time. We need "
                                " to rigorously test a strong ord=1 PGD "
                                "before enabling this feature.")

    # Use getattr() to avoid errors in eager execution attacks
    FGM = self.FGM_CLASS(
        self.model,
        sess=getattr(self, 'sess', None),
        dtypestr=self.dtypestr)

    def cond(i, _):
      """Iterate until requested number of iterations is completed"""
      return tf.less(i, self.nb_iter)

    def body(i, adv_x):
      """Do a projected gradient step"""
      adv_x = FGM.generate(adv_x, **fgm_params)

      # Clipping perturbation eta to self.ord norm ball
      eta = adv_x - x
      eta = clip_eta(eta, self.ord, self.eps)
      adv_x = x + eta

      # Redo the clipping.
      # FGM already did it, but subtracting and re-adding eta can add some
      # small numerical error.
      if self.clip_min is not None or self.clip_max is not None:
        adv_x = utils_tf.clip_by_value(adv_x, self.clip_min, self.clip_max)

      return i + 1, adv_x

    _, adv_x = tf.while_loop(cond, body, (tf.zeros([]), adv_x), back_prop=True,
                             maximum_iterations=self.nb_iter)

    # Asserts run only on CPU.
    # When multi-GPU eval code tries to force all PGD ops onto GPU, this
    # can cause an error.
    common_dtype = tf.float32
    asserts.append(utils_tf.assert_less_equal(tf.cast(self.eps_iter,
                                                      dtype=common_dtype),
                                              tf.cast(self.eps, dtype=common_dtype)))
    if self.ord == np.inf and self.clip_min is not None:
      # The 1e-6 is needed to compensate for numerical error.
      # Without the 1e-6 this fails when e.g. eps=.2, clip_min=.5,
      # clip_max=.7
      asserts.append(utils_tf.assert_less_equal(tf.cast(self.eps, x.dtype),
                                                1e-6 + tf.cast(self.clip_max,
                                                               x.dtype)
                                                - tf.cast(self.clip_min,
                                                          x.dtype)))

    if self.sanity_checks:
      with tf.control_dependencies(asserts):
        adv_x = tf.identity(adv_x)

    return adv_x