class_docstr: Feature Importance
class_docstr_tok: ["Feature","Importance"]
docstr: Add information of feature shap values to the report.
  
  Args:
      mode (str): Model Model - classification/regression model
      feature_shap_values(:list of :tuple): a list of 2-item tuple,
                                            item[0]: feature name, item[1] shap values on each training samples
      class_id(int): the class id for visualization.
      train_data(numpy.dnarray): Optional, training data, row is for samples, column is for features.
      notes(str): text to explain the block
docstr_tok: ["Add","information","of","feature","shap","values","to","the","report",".","Args",":","mode","(","str",")",":","Model","Model","-","classification\/regression","model","feature_shap_values","(",":","list","of",":","tuple",")",":","a","list","of","2-item","tuple",",","item","[","0","]",":","feature","name",",","item","[","1","]","shap","values","on","each","training","samples","class_id","(","int",")",":","the","class","id","for","visualization",".","train_data","(","numpy.dnarray",")",":","Optional",",","training","data",",","row","is","for","samples",",","column","is","for","features",".","notes","(","str",")",":","text","to","explain","the","block"]
code: def __init__(
      self,
      mode: str,
      feature_shap_values: List[Tuple[str, List]],
      class_id: int,
      train_data: numpy.ndarray,
      notes=None,
  ) -> None:
      """
      Add information of feature shap values to the report.
  
      Args:
          mode (str): Model Model - classification/regression model
          feature_shap_values(:list of :tuple): a list of 2-item tuple,
                                                item[0]: feature name, item[1] shap values on each training samples
          class_id(int): the class id for visualization.
          train_data(numpy.dnarray): Optional, training data, row is for samples, column is for features.
          notes(str): text to explain the block
      """
      super(FeatureShapValues, self).__init__(
          mode, feature_shap_values, class_id, train_data, notes
      )
      self._mode = mode
      self._feature_shap_values = feature_shap_values
      self._class_id = class_id
      self._train_data = train_data
      self._notes = notes
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from typing import List, Tuple
  import numpy
  from xai.formatter.contents.base import Content
  from xai.formatter.writer.base import Writer
label_desc: Use image node_id 1 to create a new FeatureShapValues object from inherited base classes: Content with example: obj = FeatureShapValues(mode, feature_shap_values, class_id, train_data, notes)