parameters: (self, model)
docstring: Set local _algorithms_steps variable.
          Based on algorithms configs
docstring_summary: Set local _algorithms_steps variable.
          Based on algorithms configs
docstring_tokens: Set local _algorithms_steps variable . Based on algorithms configs
function: def set_max_algorithms_steps(self, model):
          """ Set local _algorithms_steps variable.
          Based on algorithms configs"""
          skip_other_algos = False
  
          for i, algo in enumerate(self._algo_seq):
              algo.compute_total_exec_steps(model)
              self.default_steps_size = min(self.default_steps_size, algo.default_steps_size)
              if skip_other_algos:
                  continue
              algo_seq = [algo]
              if not algo.change_original_model:
                  skip_other_algos = True
                  algo_seq = self._algo_seq[i:]
  
              self._algorithms_steps += Pipeline.get_max_steps(algo_seq)
          self._algorithms_steps += self._algorithms_steps * self.default_steps_size * 2
function_tokens: def set_max_algorithms_steps ( self , model ) : skip_other_algos = False for i , algo in enumerate ( self . _algo_seq ) : algo . compute_total_exec_steps ( model ) self . default_steps_size = min ( self . default_steps_size , algo . default_steps_size ) if skip_other_algos : continue algo_seq = [ algo ] if not algo . change_original_model : skip_other_algos = True algo_seq = self . _algo_seq [ i : ] self . _algorithms_steps += Pipeline . get_max_steps ( algo_seq ) self . _algorithms_steps += self . _algorithms_steps * self . default_steps_size * 2