class_docstr: siamese neural network using Kernel Point
  Convolution to learn descriptors on patch(for registration).
code: def apply_nn(self, input, pre_computed, batch):
      data = input
      for i in range(len(self.down_modules)):
          data = self.down_modules[i](data, precomputed=pre_computed)
  
      last_feature = global_mean_pool(data.x, batch)
      output = self.FC_layer(last_feature)
      return F.normalize(output, p=2, dim=1)
  
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 4 for calling the PatchKPConv obj's underlying member method code with example usage: obj.apply_nn(input, pre_computed, batch) and returns: F