code: def create_zero_dict(compute_dirns, dimension):
      """Outputs a zero dict when neuron activation norms too small.
  
      This function creates a return_dict with appropriately shaped zero entries
      when all neuron activations are very small.
  
      Args:
                compute_dirns: boolean, whether to have zero vectors for directions
                dimension: int, defines shape of directions
  
      Returns:
                return_dict: a dict of appropriately shaped zero entries
      """
      return_dict = {}
      return_dict["mean"] = (np.asarray(0), np.asarray(0))
      return_dict["sum"] = (np.asarray(0), np.asarray(0))
      return_dict["cca_coef1"] = np.asarray(0)
      return_dict["cca_coef2"] = np.asarray(0)
      return_dict["idx1"] = 0
      return_dict["idx2"] = 0
  
      if compute_dirns:
          return_dict["cca_dirns1"] = np.zeros((1, dimension))
          return_dict["cca_dirns2"] = np.zeros((1, dimension))
  
      return return_dict
  
file_path: ecco/src/ecco/svcca_lib/cca_core.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
label_desc: Use image node_id 5 for calling a global function with example usage: create_zero_dict(compute_dirns, dimension) and returns: return_dict