docstr: Args:
  values: The list of values that represent numeric features to explain.
  Values are list of dicts with parameters or just a list of names of numeric features
docstr_tok: ["Args",":","values",":","The","list","of","values","that","represent","numeric","features","to","explain",".","Values","are","list","of","dicts","with","parameters","or","just","a","list","of","names","of","numeric","features"]
code: def setNumericFeatures(self, values: List[Union[str, Dict]]):
      """
      Args:
      values: The list of values that represent numeric features to explain.
      Values are list of dicts with parameters or just a list of names of numeric features
      """
      if len(values) == 0:
          pass
      else:
          list_values = []
          for value in values:
              if isinstance(value, str):
                  list_values.append({"name": value})
              elif isinstance(value, dict):
                  list_values.append(value)
              else:
                  pass
          self._java_obj.setNumericFeaturesPy(list_values)
      return self
  
imports: from synapse.ml.explainers._ICETransformer import _ICETransformer
  from pyspark.ml.common import inherit_doc
  from typing import List, Dict, Union
label_desc: Use image node_id 2 for calling the ICETransformer obj's underlying member method code with example usage: obj.setNumericFeatures(values) and returns: self