code: def pre_segment(self, dense_x, dense_padding_mask):
      bsz, tsz, fsz = dense_x.shape
  
      target_num = math.ceil(tsz * self.subsample_rate)
  
      rem = tsz % target_num
  
      if rem > 0:
          dense_x = F.pad(dense_x, [0, 0, 0, target_num - rem])
          dense_padding_mask = F.pad(
              dense_padding_mask, [0, target_num - rem], value=True
          )
  
      dense_x = dense_x.view(bsz, target_num, -1, fsz)
      dense_padding_mask = dense_padding_mask.view(bsz, target_num, -1)
  
      if self.cfg.mean_pool:
          dense_x = dense_x.mean(dim=-2)
          dense_padding_mask = dense_padding_mask.all(dim=-1)
      else:
          ones = torch.ones(
              (bsz, dense_x.size(2)), device=dense_x.device
          )
          indices = ones.multinomial(1)
          indices = indices.unsqueeze(-1).expand(-1, target_num, -1)
          indices_ld = indices.unsqueeze(-1).expand(-1, -1, -1, fsz)
          dense_x = dense_x.gather(2, indices_ld).reshape(bsz, -1, fsz)
          dense_padding_mask = dense_padding_mask.gather(
              2, index=indices
          ).reshape(bsz, -1)
      return dense_x, dense_padding_mask
  
imports: from dataclasses import dataclass
  from enum import Enum, auto
  import math
  import numpy
  from typing import Tuple, List, Optional, Dict
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import autograd
  from fairseq import checkpoint_utils, utils
  from fairseq.dataclass import FairseqDataclass
  from fairseq.models import BaseFairseqModel, register_model
  from fairseq.modules import SamePad, TransposeLast
label_desc: Use image node_id 1 for calling the UniformRandomSegmenter obj's underlying member method code with example usage: obj.pre_segment(dense_x, dense_padding_mask) and returns: dense_x,  dense_padding_mask