text: import torch
  from torch import nn
  import torch.nn.functional as F
  
  from .transformer_dis import FeatureEncoder
  from .transformer_dis_group import TransformerDisGroupReg
  from .NetVLAD import NetVLADWrapper
  
  
  class TransformerDisGroupNetVLAD(TransformerDisGroupReg):
      def __init__(self, *args, **kwargs):
          super().__init__(*args, **kwargs)
  
          for feature in self.feature_names:
              dim = self.feature_dims[feature]
              setattr(self, feature,
                  nn.Sequential(*[NetVLADWrapper(feature_size=dim, cluster_size=48),
                                  FeatureEncoder(dim * 48, self.gpt_dim)]))
  
      @classmethod
      def get_args(cls, args):
          args.segment_pool_type = None
          return args
  