code: def forward(self, pts):
      # divide the point cloud in the same form. This is important
      neighborhood, center = self.group_divider(pts)
      # encoder the input cloud blocks
      group_input_tokens = self.encoder(neighborhood)  # B G N
      group_input_tokens = self.reduce_dim(group_input_tokens)
      # prepare cls
      cls_tokens = self.cls_token.expand(
          group_input_tokens.size(0), -1, -1
      )
      cls_pos = self.cls_pos.expand(group_input_tokens.size(0), -1, -1)
      # add pos embedding
      pos = self.pos_embed(center)
      # final input
      x = torch.cat((cls_tokens, group_input_tokens), dim=1)
      pos = torch.cat((cls_pos, pos), dim=1)
      # transformer
      x = self.blocks(x, pos)
      x = self.norm(x)
      concat_f = torch.cat([x[:, 0], x[:, 1:].max(1)[0]], dim=-1)
      # ret = self.cls_head_finetune(concat_f)
      return concat_f
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from timm.models.layers import DropPath
  from lavis.models.ulip_models.pointbert.dvae import Group
  from lavis.models.ulip_models.pointbert.dvae import Encoder
  from lavis.models.ulip_models.pointbert.logger import print_log
  from lavis.models.ulip_models.pointbert.checkpoint import get_missing_parameters_message, get_unexpected_parameters_message
label_desc: Use image node_id 5 for calling the PointTransformer obj's underlying member method code with example usage: obj.forward(pts) and returns: concat_f