code: def _prepare_features(self, x, pos, new_pos, idx):
      new_pos_trans = pos.transpose(1, 2).contiguous()
      grouped_pos_absolute = tp.grouping_operation(
          new_pos_trans, idx
      )  # (B, 3, npoint, nsample)
      centroids = new_pos.transpose(1, 2).unsqueeze(-1)
      grouped_pos_normalized = grouped_pos_absolute - centroids
  
      if x is not None:
          grouped_features = tp.grouping_operation(x, idx)
          if self.use_xyz:
              new_features = torch.cat(
                  [
                      grouped_pos_absolute,
                      grouped_pos_normalized,
                      grouped_features,
                  ],
                  dim=1,
              )  # (B, 3 + 3 + C, npoint, nsample)
          else:
              new_features = grouped_features
      else:
          assert (
              self.use_xyz
          ), "Cannot have not features and not use xyz as a feature!"
          new_features = torch.cat(
              [grouped_pos_absolute, grouped_pos_normalized], dim=1
          )  # (B, 3 + 3 npoint, nsample)
  
      return new_features, centroids
  
imports: import logging
  import torch
  from torch.nn import Sequential
  import torch.nn
  import torch.nn.functional
  from torch.nn import Conv1d
  import torch_points_kernels
  from torch_points3d.core.base_conv.dense import *
  from torch_points3d.core.common_modules.dense_modules import MLP2D
  from torch_points3d.core.spatial_ops import DenseFPSSampler, DenseRadiusNeighbourFinder
  from torch_points3d.utils.colors import COLORS
label_desc: Use image node_id 2 for calling the RSConvMSGDown obj's underlying member method code with example usage: obj._prepare_features(x, pos, new_pos, idx) and returns: new_features,  centroids