code: def add_trainable_vars_summary(
      variables, name_prefix="", name_suffix="", collection_key=None
  ):
      """add_trainable_vars_summary.
  
      Add histogram summary for given variables weights.
  
      Arguments:
          variables: A list of `Variable`. The variables to summarize.
          name_prefix: `str`. A prefix to add to summary scope.
          name_suffix: `str`. A suffix to add to summary scope.
          collection_key: `str`. A collection to store the summaries.
  
      Returns:
          The list of created weights summaries.
  
      """
  
      # Add histograms for trainable variables.
      summ = []
      for var in variables:
          summ_name = format_scope_name(
              var.op.name, name_prefix, name_suffix
          )
          summ_exists = summary_exists(summ_name)
          if summ_exists is not None:
              tf.add_to_collection(collection_key, summ_exists)
              summ.append(summ_exists)
          else:
              summ.append(
                  get_summary(
                      "histogram", summ_name, var, collection_key
                  )
              )
      return summ
  
file_path: tflearn/tflearn/summaries.py
imports: from __future__ import division, print_function, absolute_import
  import tensorflow.compat.v1
  from tensorflow.core.framework import summary_pb2
  from .utils import format_scope_name
label_desc: Use image node_id 5 for calling a global function with example usage: add_trainable_vars_summary(variables, name_prefix, name_suffix, collection_key) and returns: summ