code: def summarize_activation(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)
  
file_path: tensornets/tensornets/contrib_layers/summaries.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  import re
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import standard_ops
  from tensorflow.python.summary import summary
label_desc: Use image node_id 3 for calling a global function with example usage: summarize_activation(op) and returns: _add_histogram_summary