code: def convert_decision_ensemble_tree_common(
      operator,
      tree_infos,
      get_parameters,
      get_parameters_for_tree_trav,
      n_features,
      classes=None,
      extra_config={},
  ):
      tree_parameters, max_depth, tree_type = get_tree_params_and_type(
          tree_infos, get_parameters, extra_config
      )
  
      # Generate the tree implementation based on the selected strategy.
      if tree_type == TreeImpl.gemm:
          net_parameters = [
              get_parameters_for_gemm_common(
                  tree_param.lefts,
                  tree_param.rights,
                  tree_param.features,
                  tree_param.thresholds,
                  tree_param.values,
                  n_features,
                  extra_config,
              )
              for tree_param in tree_parameters
          ]
          return GEMMDecisionTreeImpl(
              operator,
              net_parameters,
              n_features,
              classes,
              extra_config=extra_config,
          )
  
      net_parameters = [
          get_parameters_for_tree_trav(
              tree_param.lefts,
              tree_param.rights,
              tree_param.features,
              tree_param.thresholds,
              tree_param.values,
              extra_config,
          )
          for tree_param in tree_parameters
      ]
      if tree_type == TreeImpl.tree_trav:
          return TreeTraversalDecisionTreeImpl(
              operator,
              net_parameters,
              max_depth,
              n_features,
              classes,
              extra_config,
          )
      else:  # Remaining possible case: tree_type == TreeImpl.perf_tree_trav
          return PerfectTreeTraversalDecisionTreeImpl(
              operator,
              net_parameters,
              max_depth,
              n_features,
              classes,
              extra_config,
          )
  
imports: import copy
  import numpy
  import torch
  from ._tree_implementations import TreeImpl
  from ._tree_implementations import GEMMDecisionTreeImpl, TreeTraversalDecisionTreeImpl, PerfectTreeTraversalDecisionTreeImpl
  from .None import constants
  from hummingbird.ml.exceptions import MissingConverter
label_desc: Use image node_id 9 for calling a global function with example usage: convert_decision_ensemble_tree_common(operator, tree_infos, get_parameters, get_parameters_for_tree_trav, n_features, classes, extra_config) and returns: GEMMDecisionTreeImpl, TreeTraversalDecisionTreeImpl, PerfectTreeTraversalDecisionTreeImpl