code: def linear_shap_corr(model, data):
      """Linear SHAP (corr 1000)"""
      return LinearExplainer(
          model, data, feature_dependence="correlation", nsamples=1000
      ).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 1 for calling a global function with example usage: linear_shap_corr(model, data) and returns: LinearExplainer