tensorflow_gan/python/losses/losses_impl.py [509:546]:
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    real_weights=1.0,
    generated_weights=1.0,
    scope=None,
    loss_collection=tf.compat.v1.GraphKeys.LOSSES,
    reduction=tf.compat.v1.losses.Reduction.SUM_BY_NONZERO_WEIGHTS,
    add_summaries=False):
  """Original minimax discriminator loss for GANs, with label smoothing.

  Note that the authors don't recommend using this loss. A more practically
  useful loss is `modified_discriminator_loss`.

  L = - real_weights * log(sigmoid(D(x)))
      - generated_weights * log(1 - sigmoid(D(G(z))))

  See `Generative Adversarial Nets` (https://arxiv.org/abs/1406.2661) for more
  details.

  Args:
    discriminator_real_outputs: Discriminator output on real data.
    discriminator_gen_outputs: Discriminator output on generated data. Expected
      to be in the range of (-inf, inf).
    label_smoothing: The amount of smoothing for positive labels. This technique
      is taken from `Improved Techniques for Training GANs`
      (https://arxiv.org/abs/1606.03498). `0.0` means no smoothing.
    real_weights: Optional `Tensor` whose rank is either 0, or the same rank as
      `real_data`, and must be broadcastable to `real_data` (i.e., all
      dimensions must be either `1`, or the same as the corresponding
      dimension).
    generated_weights: Same as `real_weights`, but for `generated_data`.
    scope: The scope for the operations performed in computing the loss.
    loss_collection: collection to which this loss will be added.
    reduction: A `tf.losses.Reduction` to apply to loss.
    add_summaries: Whether or not to add summaries for the loss.

  Returns:
    A loss Tensor. The shape depends on `reduction`.
  """
  with tf.compat.v1.name_scope(
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tensorflow_gan/python/losses/losses_impl.py [782:818]:
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    real_weights=1.0,
    generated_weights=1.0,
    scope=None,
    loss_collection=tf.compat.v1.GraphKeys.LOSSES,
    reduction=tf.compat.v1.losses.Reduction.SUM_BY_NONZERO_WEIGHTS,
    add_summaries=False):
  """Least squares discriminator loss.

  This loss comes from `Least Squares Generative Adversarial Networks`
  (https://arxiv.org/abs/1611.04076).

  L = 1/2 * (D(x) - `real`) ** 2 +
      1/2 * (D(G(z)) - `fake_label`) ** 2

  where D(y) are discriminator logits.

  Args:
    discriminator_real_outputs: Discriminator output on real data.
    discriminator_gen_outputs: Discriminator output on generated data. Expected
      to be in the range of (-inf, inf).
    real_label: The value that the discriminator tries to output for real data.
    fake_label: The value that the discriminator tries to output for fake data.
    real_weights: Optional `Tensor` whose rank is either 0, or the same rank as
      `discriminator_real_outputs`, and must be broadcastable to
      `discriminator_real_outputs` (i.e., all dimensions must be either `1`, or
      the same as the corresponding dimension).
    generated_weights: Same as `real_weights`, but for
      `discriminator_gen_outputs`.
    scope: The scope for the operations performed in computing the loss.
    loss_collection: collection to which this loss will be added.
    reduction: A `tf.losses.Reduction` to apply to loss.
    add_summaries: Whether or not to add summaries for the loss.

  Returns:
    A loss Tensor. The shape depends on `reduction`.
  """
  with tf.compat.v1.name_scope(
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