code: def forward(self, data, **kwargs):
      x, pos = data.x, data.pos
      pos_flipped = pos.transpose(1, 2).contiguous()
  
      x = self.nn(torch.cat([x, pos_flipped], dim=1).unsqueeze(-1))
  
      if self._aggr == "max":
          x = x.squeeze(-1).max(-1)[0]
      elif self._aggr == "mean":
          x = x.squeeze(-1).mean(-1)
      else:
          raise NotImplementedError(
              "The following aggregation {} is not recognized".format(
                  self._aggr
              )
          )
  
      pos = None  # pos.mean(1).unsqueeze(1)
      x = x.unsqueeze(-1)
      return Data(x=x, pos=pos)
  
imports: import numpy
  import torch
  from torch.nn import Linear, ReLU, LeakyReLU, BatchNorm1d, Dropout
  from torch_geometric.nn import knn_interpolate, fps, radius, global_max_pool, global_mean_pool, knn
  from torch_geometric.data import Data
  import torch_points_kernels
  from torch_points3d.core.spatial_ops import BaseMSNeighbourFinder
  from torch_points3d.core.base_conv import BaseConvolution
  from torch_points3d.core.common_modules.dense_modules import MLP2D
  from torch_points3d.utils.enums import ConvolutionFormat
  from torch_points3d.utils.model_building_utils.activation_resolver import get_activation
label_desc: Use image node_id 3 for calling the GlobalDenseBaseModule obj's underlying member method code with example usage: obj.forward(data) and returns: Data