text: import torch.nn as nn
  import torch.utils.data
  import torch.nn.functional as F
  from pointnet_utils import PointNetEncoder, feature_transform_reguliarzer
  
  class get_model(nn.Module):
      def __init__(self, k=40, normal_channel=True):
          super(get_model, self).__init__()
          if normal_channel:
              channel = 6
          else:
              channel = 3
          self.feat = PointNetEncoder(global_feat=True, feature_transform=True, channel=channel)
          self.fc1 = nn.Linear(1024, 512)
          self.fc2 = nn.Linear(512, 256)
          self.fc3 = nn.Linear(256, k)
          self.dropout = nn.Dropout(p=0.4)
          self.bn1 = nn.BatchNorm1d(512)
          self.bn2 = nn.BatchNorm1d(256)
          self.relu = nn.ReLU()
  
      def forward(self, x):
          x, trans, trans_feat = self.feat(x)
          x = F.relu(self.bn1(self.fc1(x)))
          x = F.relu(self.bn2(self.dropout(self.fc2(x))))
          x = self.fc3(x)
          x = F.log_softmax(x, dim=1)
          return x, trans_feat
  
  class get_loss(torch.nn.Module):
      def __init__(self, mat_diff_loss_scale=0.001):
          super(get_loss, self).__init__()
          self.mat_diff_loss_scale = mat_diff_loss_scale
  
      def forward(self, pred, target, trans_feat):
          loss = F.nll_loss(pred, target)
          mat_diff_loss = feature_transform_reguliarzer(trans_feat)
  
          total_loss = loss + mat_diff_loss * self.mat_diff_loss_scale
          return total_loss
  