class_docstr: Suggests an architecture based on the Harmonica algorithm.
docstr: Returns a list of the relevant variables indices based on coeff.
docstr_tok: ["Returns","a","list","of","the","relevant","variables","indices","based","on","coeff","."]
code: def _extract_relevant_variables_indices(
      self, feature_extender, coefficients
  ):
      """Returns a list of the relevant variables indices based on coeff."""
      all_variable_names = feature_extender.get_feature_names()
      relevant_variables = []
      for i, coeff in enumerate(coefficients):
          if coeff > 0:
              relevant_variables += self._parse_variable_name(
                  all_variable_names[i]
              )
      return set(relevant_variables)
  
imports: import cmath
  import os
  from absl import logging
  from model_search import block_builder
  from model_search.architecture import architecture_utils
  from model_search.search import search_algorithm
  import numpy
  from sklearn import linear_model
  from sklearn import preprocessing
  import tensorflow.compat.v2
label_desc: Use image node_id 7 for calling the Harmonica obj's underlying member method code with example usage: obj._extract_relevant_variables_indices(feature_extender, coefficients) and returns: set