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 batch_norm(inputs, training, data_format, name=''):
  """Performs a batch normalization using a standard set of parameters."""
  # We set fused=True for a significant performance boost. See
  # https://www.tensorflow.org/performance/performance_guide#common_fused_ops
  return tf.compat.v1.layers.batch_normalization(
      inputs=inputs,
      axis=1 if data_format == 'channels_first' else 3,
      momentum=_BATCH_NORM_DECAY,
      epsilon=_BATCH_NORM_EPSILON,
      center=True,
      scale=True,
      training=training,
      fused=True,
      name=name)