(batch_size=gin.REQUIRED,
                     gan_class=gin.REQUIRED,
                     architecture=gin.REQUIRED,
                     training_steps=gin.REQUIRED,
                     discriminator_normalization=None,
                     lamba=1,
                     disc_iters=1,
                     z_dim=128)
return {
      "use_tpu": FLAGS.use_tpu,  # For compatibility with AbstractGAN.
      "batch_size": batch_size,
      "gan_class": gan_class,
      "architecture": architecture,
      "training_steps": training_steps,
      "lambda": lamba,  # Different spelling intended.
      "disc_iters": disc_iters,
      "z_dim": z_dim,
  }
Parse legacy options from Gin configurations into a Python dict.

  Args:
    batch_size: The (global) batch size to use. On TPUs each core will get a
      fraction of this.
    gan_class: References to the GAN classe to use. This must implement the
      AbstractGAN interface.
    architecture: Name of the architecuter to use for G and D. This should be
      value from consts.ARCHITECTURES and be supported by the GAN class.
    training_steps: The number of training steps. These are discriminator steps.
    discriminator_normalization: Deprecated. Ignored, but kept to read old
      configs.
    lamba: Weight for gradient penalty.
    disc_iters: How often the discriminator is trained before training G for one
      step. G will be trained for `training_steps // disc_iters` steps.
    z_dim: Length of the latent variable z fed to the generator.

  Returns:
    A Python dictionary with the options.
Parse legacy options from Gin configurations into a Python dict.
Parse legacy options from Gin configurations into a Python dict .
def get_options_dict(batch_size=gin.REQUIRED,
                     gan_class=gin.REQUIRED,
                     architecture=gin.REQUIRED,
                     training_steps=gin.REQUIRED,
                     discriminator_normalization=None,
                     lamba=1,
                     disc_iters=1,
                     z_dim=128):
  """Parse legacy options from Gin configurations into a Python dict.

  Args:
    batch_size: The (global) batch size to use. On TPUs each core will get a
      fraction of this.
    gan_class: References to the GAN classe to use. This must implement the
      AbstractGAN interface.
    architecture: Name of the architecuter to use for G and D. This should be
      value from consts.ARCHITECTURES and be supported by the GAN class.
    training_steps: The number of training steps. These are discriminator steps.
    discriminator_normalization: Deprecated. Ignored, but kept to read old
      configs.
    lamba: Weight for gradient penalty.
    disc_iters: How often the discriminator is trained before training G for one
      step. G will be trained for `training_steps // disc_iters` steps.
    z_dim: Length of the latent variable z fed to the generator.

  Returns:
    A Python dictionary with the options.
  """
  del discriminator_normalization
  return {
      "use_tpu": FLAGS.use_tpu,  # For compatibility with AbstractGAN.
      "batch_size": batch_size,
      "gan_class": gan_class,
      "architecture": architecture,
      "training_steps": training_steps,
      "lambda": lamba,  # Different spelling intended.
      "disc_iters": disc_iters,
      "z_dim": z_dim,
  }
def get_options_dict ( batch_size = gin . REQUIRED , gan_class = gin . REQUIRED , architecture = gin . REQUIRED , training_steps = gin . REQUIRED , discriminator_normalization = None , lamba = 1 , disc_iters = 1 , z_dim = 128 ) : del discriminator_normalization return { "use_tpu" : FLAGS . use_tpu , # For compatibility with AbstractGAN. "batch_size" : batch_size , "gan_class" : gan_class , "architecture" : architecture , "training_steps" : training_steps , "lambda" : lamba , # Different spelling intended. "disc_iters" : disc_iters , "z_dim" : z_dim , }