def unIndentBlock(block):
            currentIndent = blockIndentation(block)

            if currentIndent.endswith('\t'):
                charsToRemove = 1
            elif withSpace:
                charsToRemove = 1 if currentIndent else 0
            else:
                if self.useTabs:
                    charsToRemove = min(spacesCount(currentIndent), self.width)
                else:  # spaces
                    if currentIndent.endswith(self.text()):  # remove indent level
                        charsToRemove = self.width
                    else:  # remove all spaces
                        charsToRemove = min(spacesCount(currentIndent), self.width)

            if charsToRemove:
                cursor = cursorAtSpaceEnd(block)
                cursor.setPosition(cursor.position() - charsToRemove, QTextCursor.KeepAnchor)
                cursor.removeSelectedText()
def run_model(prior, images, model, disc_reg_weight):
  """Run the model with new data and samples.

  Args:
    prior: the noise source as the generator input.
    images: images sampled from dataset.
    model: a GAN model defined in gan.py.
    disc_reg_weight: regularisation weight for discrmininator gradients.

  Returns:
    debug_ops: statistics from the model, see gan.py for more detials.
    disc_grads: discriminator gradients.
    gen_grads: generator gradients.
  """
  generator_inputs = prior.sample(FLAGS.batch_size)
  model_output = model.connect(images, generator_inputs)
  optimization_components = model_output.optimization_components

  disc_grads = tf.gradients(
      optimization_components['disc'].loss,
      optimization_components['disc'].vars)

  gen_grads = tf.gradients(
      optimization_components['gen'].loss,
      optimization_components['gen'].vars)

  if disc_reg_weight > 0.0:
    reg_grads = _compute_reg_grads(gen_grads,
                                   optimization_components['disc'].vars)
    disc_grads = _acc_grads(disc_grads, disc_reg_weight, reg_grads)

  debug_ops = model_output.debug_ops

  return debug_ops, disc_grads, gen_grads