code: def sort_samples_and_feature_values(self, current_feature):
      feature_values = self.feature_values
      X = self.X
      samples = self.samples
      n_missing = 0
      missing_values_in_feature_mask = (
          self.missing_values_in_feature_mask
      )
      if (
          missing_values_in_feature_mask is not None
          and missing_values_in_feature_mask[current_feature]
      ):
          i, current_end = self.start, self.end - 1
          while i <= current_end:
              if ivy.isnan(X[samples[current_end], current_feature]):
                  n_missing += 1
                  current_end -= 1
                  continue
              if ivy.isnan(X[samples[i], current_feature]):
                  samples[i], samples[current_end] = (
                      samples[current_end],
                      samples[i],
                  )
                  n_missing += 1
                  current_end -= 1
              feature_values[i] = X[samples[i], current_feature]
              i += 1
      else:
          for i in range(self.start, self.end):
              feature_values[i] = X[
                  int(samples[i]), int(current_feature)
              ]
      (
          self.feature_values[self.start : self.end],
          self.samples[self.start : self.end],
      ) = sort(
          feature_values[self.start : self.end],
          samples[self.start : self.end],
          self.end - self.start - n_missing,
      )
      self.n_missing = n_missing
  
imports: import ivy
label_desc: Use image node_id 3 for calling the DensePartitioner obj's underlying member method code with example usage: obj.sort_samples_and_feature_values(current_feature) without return types