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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 attack_single_step(self, x, eta, g_feat):
 """ TensorFlow implementation of the Fast Feature Gradient. This is a single step attack similar to Fast Gradient Method that attacks an internal representation. :param x: the input placeholder :param eta: A tensor the same shape as x that holds the perturbation. :param g_feat: model's internal tensor for guide :return: a tensor for the adversarial example """


    adv_x = x + eta
    a_feat = self.model.fprop(adv_x)[self.layer]

    # feat.shape = (batch, c) or (batch, w, h, c)
    axis = list(range(1, len(a_feat.shape)))

    # Compute loss
    # This is a targeted attack, hence the negative sign
    loss = -reduce_sum(tf.square(a_feat - g_feat), axis)

    # Define gradient of loss wrt input
    grad, = tf.gradients(loss, adv_x)

    # Multiply by constant epsilon
    scaled_signed_grad = self.eps_iter * tf.sign(grad)

    # Add perturbation to original example to obtain adversarial example
    adv_x = adv_x + scaled_signed_grad

    # If clipping is needed,
    # reset all values outside of [clip_min, clip_max]
    if (self.clip_min is not None) and (self.clip_max is not None):
      adv_x = tf.clip_by_value(adv_x, self.clip_min, self.clip_max)

    adv_x = tf.stop_gradient(adv_x)

    eta = adv_x - x
    eta = clip_eta(eta, self.ord, self.eps)

    return eta