def METHOD_NAME(op):
  """Summarize an activation.
  This applies the given activation and adds useful summaries specific to the
  activation.
  Args:
    op: The tensor to summarize (assumed to be a layer activation).
  Returns:
    The summary op created to summarize `op`.
  """
  if op.op.type in ('Relu', 'Softplus', 'Relu6'):
    # Using inputs to avoid floating point equality and/or epsilons.
    _add_scalar_summary(
        standard_ops.reduce_mean(
            standard_ops.to_float(
                standard_ops.less(op.op.inputs[
                    0], standard_ops.cast(0.0, op.op.inputs[0].dtype)))),
        '%s/zeros' % op.op.name)
  if op.op.type == 'Relu6':
    _add_scalar_summary(
        standard_ops.reduce_mean(
            standard_ops.to_float(
                standard_ops.greater(op.op.inputs[
                    0], standard_ops.cast(6.0, op.op.inputs[0].dtype)))),
        '%s/sixes' % op.op.name)
  return _add_histogram_summary(op, '%s/activation' % op.op.name)