class_docstr: siamese neural network using Kernel Point
  Convolution to learn descriptors on patch(for registration).
code: def __init__(self, option, model_type, dataset, modules):
      BackboneBasedModel.__init__(
          self, option, model_type, dataset, modules
      )
      self.set_last_mlp(option.mlp_cls)
      self.loss_names = ["loss_reg", "loss", "internal"]
  
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 1 to create a new PatchKPConv object from inherited base classes: BackboneBasedModel with example: obj = PatchKPConv(option, model_type, dataset, modules)