class_docstr: Suggests an architecture based on the Harmonica algorithm.
docstr: Returns the indices that form the variable given the name.
docstr_tok: ["Returns","the","indices","that","form","the","variable","given","the","name","."]
code: def _parse_variable_name(self, name):
      """Returns the indices that form the variable given the name."""
      # Bias
      if name == "1":
          return []
      # Names are of the form 'x1 x6 x8'
      else:
          variables = name.split(" ")
          return [int(varname[1:]) for varname in 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 6 for calling the Harmonica obj's underlying member method code with example usage: obj._parse_variable_name(name) and returns: list, unknown