class_docstr: Simply wrap of lime.lime_tabular.LimeTabularExplainer into the common shap interface.
  
  Parameters
  ----------
  model : function or iml.Model
      User supplied function that takes a matrix of samples (# samples x # features) and
      computes the output of the model for those samples. The output can be a vector
      (# samples) or a matrix (# samples x # model outputs).
  
  data : numpy.array
      The background dataset.
  
  mode : "classification" or "regression"
      Control the mode of LIME tabular.
code: def attributions(self, X, nsamples=5000, num_features=None):
      num_features = (
          X.shape[1] if num_features is None else num_features
      )
  
      if isinstance(X, pd.DataFrame):
          X = X.values
  
      out = [np.zeros(X.shape) for j in range(self.out_dim)]
      for i in range(X.shape[0]):
          exp = self.explainer.explain_instance(
              X[i],
              self.model,
              labels=range(self.out_dim),
              num_features=num_features,
          )
          for j in range(self.out_dim):
              for k, v in exp.local_exp[j]:
                  out[j][i, k] = v
  
      # because it output two results even for only one model output, and they are negated from what we expect
      if self.mode == "regression":
          for i in range(len(out)):
              out[i] = -out[i]
  
      return out[0] if self.flat_out else out
  
imports: import numpy
  import pandas
  from .._explainer import Explainer
label_desc: Use image node_id 2 for calling the LimeTabular obj's underlying member method code with example usage: obj.attributions(X, nsamples, num_features) and returns: unknown