docstr: This method does a forward on the Unet
  
  Parameters:
  -----------
  data
      A dictionary that contains the data itself and its metadata information. Should contain
          x -- Features [B, N, C]
          pos -- Points [B, N, 3]
docstr_tok: ["This","method","does","a","forward","on","the","Unet","Parameters",":","--","--","--","--","--","-","data","A","dictionary","that","contains","the","data","itself","and","its","metadata","information",".","Should","contain","x","--","Features","[","B",",","N",",","C","]","pos","--","Points","[","B",",","N",",","3","]"]
code: def forward(self, data, *args, **kwargs):
      """This method does a forward on the Unet
  
      Parameters:
      -----------
      data
          A dictionary that contains the data itself and its metadata information. Should contain
              x -- Features [B, N, C]
              pos -- Points [B, N, 3]
      """
      self._set_input(data)
      stack_down = []
      queue_up = queue.Queue()
  
      data = self.input
      stack_down.append(data)
  
      for i in range(len(self.down_modules) - 1):
          data = self.down_modules[i](data)
          stack_down.append(data)
      sampling_ids = self._collect_sampling_ids(stack_down)
  
      data = self.down_modules[-1](data)
      queue_up.put(data)
  
      assert (
          len(self.inner_modules) == 2
      ), "For this segmentation model, we except 2 distinct inner"
      data_inner = self.inner_modules[0](data)
      data_inner_2 = self.inner_modules[1](stack_down[3])
  
      for i in range(len(self.up_modules)):
          data = self.up_modules[i]((queue_up.get(), stack_down.pop()))
          queue_up.put(data)
  
      last_feature = torch.cat(
          [
              data.x,
              data_inner.x.repeat(1, 1, data.x.shape[-1]),
              data_inner_2.x.repeat(1, 1, data.x.shape[-1]),
          ],
          dim=1,
      )
  
      if self.has_mlp_head:
          data.x = self.mlp(last_feature)
      else:
          data.x = last_feature
      for key, value in sampling_ids.items():
          setattr(data, key, value)
      return data
  
imports: import os
  import sys
  import queue
  from omegaconf import DictConfig, OmegaConf
  import logging
  from torch_points3d.applications.modelfactory import ModelFactory
  from torch_points3d.modules.RSConv import *
  from torch_points3d.core.base_conv.dense import DenseFPModule
  from torch_points3d.models.base_architectures.unet import UnwrappedUnetBasedModel
  from torch_points3d.datasets.multiscale_data import MultiScaleBatch
  from torch_points3d.core.common_modules.dense_modules import Conv1D
  from torch_points3d.core.common_modules.base_modules import Seq
  from .utils import extract_output_nc
label_desc: Use image node_id 2 for calling the RSConvUnet obj's underlying member method code with example usage: obj.forward(data) and returns: data