(block_id, progress)
return tf.clip_by_value(block_id - progress, 0.0, 1.0)
Returns the block input parameter for discriminator network.

  The discriminator has N blocks with `block_id` = 1,2,...,N. Each block
  block_id accepts an
    - input(block_id) transformed from the real data and
    - the output of block block_id + 1, i.e. output(block_id + 1)
  The final input is a linear combination of them,
  i.e. alpha * input(block_id) + (1 - alpha) * output(block_id + 1)
  where alpha = _discriminator_alpha(block_id, progress).

  With a fixed block_id, alpha(block_id, progress) stays to be 1
  when progress <= block_id - 1, then linear decays to 0 when
  block_id - 1 < progress <= block_id, and finally stays at 0
  when progress > block_id.

  Args:
    block_id: An integer of generator block id.
    progress: A scalar float `Tensor` of training progress.

  Returns:
    A scalar float `Tensor` of block input parameter.
Returns the block input parameter for discriminator network.
Returns the block input parameter for discriminator network .
def _discriminator_alpha(block_id, progress):
  """Returns the block input parameter for discriminator network.

  The discriminator has N blocks with `block_id` = 1,2,...,N. Each block
  block_id accepts an
    - input(block_id) transformed from the real data and
    - the output of block block_id + 1, i.e. output(block_id + 1)
  The final input is a linear combination of them,
  i.e. alpha * input(block_id) + (1 - alpha) * output(block_id + 1)
  where alpha = _discriminator_alpha(block_id, progress).

  With a fixed block_id, alpha(block_id, progress) stays to be 1
  when progress <= block_id - 1, then linear decays to 0 when
  block_id - 1 < progress <= block_id, and finally stays at 0
  when progress > block_id.

  Args:
    block_id: An integer of generator block id.
    progress: A scalar float `Tensor` of training progress.

  Returns:
    A scalar float `Tensor` of block input parameter.
  """
  return tf.clip_by_value(block_id - progress, 0.0, 1.0)
def _discriminator_alpha ( block_id , progress ) : return tf . clip_by_value ( block_id - progress , 0.0 , 1.0 )