code: def get_parameters_for_tree_trav_sklearn(
      lefts,
      rights,
      features,
      thresholds,
      values,
      classes=None,
      extra_config={},
  ):
      """
      This function is used to generate tree parameters for sklearn trees.
      Includes SklearnRandomForestClassifier/Regressor, and SklearnGradientBoostingClassifier.
  
      Args:
          left: The left nodes
          right: The right nodes
          features: The features used in the decision nodes
          thresholds: The thresholds used in the decision nodes
          values: The values stored in the leaf nodes
          classes: The list of class labels. None if regression model
      Returns:
          An array containing the extracted parameters
      """
      features = [max(x, 0) for x in features]
      values = np.array(values)
      if len(values.shape) == 3:
          values = values.reshape(values.shape[0], -1)
      if (
          values.shape[1] > 1
          and classes is not None
          and len(classes) > 0
      ):
          # Triggers only for classification.
          values /= np.sum(values, axis=1, keepdims=True)
      if constants.NUM_TREES in extra_config:
          values /= extra_config[constants.NUM_TREES]
  
      return get_parameters_for_tree_trav_common(
          lefts, rights, features, thresholds, values
      )
  
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 7 for calling a global function with example usage: get_parameters_for_tree_trav_sklearn(lefts, rights, features, thresholds, values, classes, extra_config) and returns: get_parameters_for_tree_trav_common