docs: def deserialize_single_input_output(self, transformer, node_path, attributes_map=None):
          """
          :attributes_map: Map of attributes names. For example StandardScaler has `mean_` but is serialized as `mean`
          :param transformer: Scikit or Pandas transformer
          :param node: bundle.ml node json file
          :param model: bundle.ml model json file
          :return: Transformer
          """
          # Load the model file
          with open("{}/model.json".format(node_path)) as json_data:
              model_j = json.load(json_data)
  
          # Set Transformer Attributes
          attributes = model_j['attributes']
          for attribute in attributes.keys():
              value_key = [key for key in attributes[attribute].keys()
                           if key in ['string', 'boolean','long', 'double', 'data_shape']][0]
              if attributes_map is not None and attribute in attributes_map.keys():
                  setattr(transformer, attributes_map[attribute], attributes[attribute][value_key])
              else:
                  setattr(transformer, attribute, attributes[attribute][value_key])
  
          transformer.op = model_j['op']
  
          # Load the node file
          with open("{}/node.json".format(node_path)) as json_data:
              node_j = json.load(json_data)
  
          transformer.name = node_j['name']
          transformer.input_features = self._node_features_format(node_j['shape']['inputs'][0]['name'])
          transformer.output_features = self._node_features_format(node_j['shape']['outputs'][0]['name'])
  
          return transformer