code: def apply_nn(self, input):
      output = self.model(input).F
      output = self.FC_layer(output)
      if self.normalize_feature:
          return output / (
              torch.norm(output, p=2, dim=1, keepdim=True) + 1e-3
          )
      else:
          return output
  
imports: import logging
  import torch
  from torch_geometric.data import Data
  from torch_points3d.modules.MinkowskiEngine import *
  from torch_points3d.models.base_architectures import UnwrappedUnetBasedModel
  from torch_points3d.models.registration.base import FragmentBaseModel
  from torch.nn import Sequential, Linear, LeakyReLU, Dropout
  from torch_points3d.core.common_modules import FastBatchNorm1d, Seq
label_desc: Use image node_id 2 for calling the Minkowski_Baseline_Model_Fragment obj's underlying member method code with example usage: obj.apply_nn(input) and returns: unknown, output