code: def create_pretrain_head(self, head_nf, vars, dropout):
      return nn.Sequential(
          nn.Dropout(dropout), nn.Conv1d(head_nf, vars, 1)
      )
  
imports: import math
  import numpy
  from typing import Optional
  import torch
  import torch.nn
  import torch.nn.functional
  from ..common._base_windows import BaseWindows
  from ..losses.pytorch import MAE
label_desc: Use image node_id 3 for calling the PatchTST_backbone obj's underlying member method code with example usage: obj.create_pretrain_head(head_nf, vars, dropout) and returns: nn