class_docstr: siamese neural network using Kernel Point
  Convolution to learn descriptors on patch(for registration).
code: def set_last_mlp(self, last_mlp_opt):
      if len(last_mlp_opt.nn) > 2:
          self.FC_layer = MLP(
              last_mlp_opt.nn[: len(last_mlp_opt.nn) - 1]
          )
          self.FC_layer.add_module(
              "last", Lin(last_mlp_opt.nn[-2], last_mlp_opt.nn[-1])
          )
      elif len(last_mlp_opt.nn) == 2:
          self.FC_layer = Seq(
              Lin(last_mlp_opt.nn[-2], last_mlp_opt.nn[-1])
          )
      else:
          self.FC_layer = torch.nn.Identity()
  
imports: from typing import Any
  import torch
  import torch.nn.functional
  from torch.nn import Linear
  from torch.nn import Sequential
  from torch_geometric.data import Data
  from torch_geometric.nn import global_mean_pool
  import logging
  from torch_points3d.core.losses import *
  from torch_points3d.core.common_modules import MLP
  from torch_points3d.core.common_modules import FastBatchNorm1d
  from torch_points3d.core.base_conv.partial_dense import *
  from torch_points3d.models.registration.base import FragmentBaseModel
  from torch_points3d.models.base_architectures import BackboneBasedModel
  from torch_points3d.models.registration.base import create_batch_siamese
  from torch_points3d.models.base_model import BaseModel
  from torch_points3d.models.base_architectures.unet import UnwrappedUnetBasedModel
  from torch_points3d.modules.KPConv import *
  from torch_points3d.datasets.registration.pair import PairMultiScaleBatch
label_desc: Use image node_id 2 for calling the PatchKPConv obj's underlying member method code with example usage: obj.set_last_mlp(last_mlp_opt) without return types