code: def find_min_max(
      self,
      current_feature: int,
      min_feature_value_out: float,
      max_feature_value_out: float,
  ):
      current_feature = 0
      X = self.X
      samples = self.samples
      min_feature_value = X[samples[self.start], current_feature]
      max_feature_value = min_feature_value
      feature_values = self.feature_values
      feature_values[self.start] = min_feature_value
      for p in range(self.start + 1, self.end):
          current_feature_value = X[samples[p], current_feature]
          feature_values[p] = current_feature_value
  
          if current_feature_value < min_feature_value:
              min_feature_value = current_feature_value
          elif current_feature_value > max_feature_value:
              max_feature_value = current_feature_value
      return min_feature_value, max_feature_value
  
imports: import ivy
label_desc: Use image node_id 4 for calling the DensePartitioner obj's underlying member method code with example usage: obj.find_min_max(current_feature, min_feature_value_out, max_feature_value_out) and returns: min_feature_value,  max_feature_value