code: def tree_shap_independent_200(model, data):
      """TreeExplainer (independent)
      color = red_blue_circle(0)
      linestyle = dashed
      """
      data_subsample = sklearn.utils.resample(
          data,
          replace=False,
          n_samples=min(200, data.shape[0]),
          random_state=0,
      )
      return TreeExplainer(
          model, data_subsample, feature_dependence="independent"
      ).shap_values
  
imports: import numpy
  import sklearn
  from ..None import DeepExplainer, GradientExplainer, KernelExplainer, LinearExplainer, SamplingExplainer, TreeExplainer, kmeans
  from ..explainers import other
  from .models import KerasWrap
label_desc: Use image node_id 8 for calling a global function with example usage: tree_shap_independent_200(model, data) and returns: TreeExplainer