code: def __init__(
      self,
      c_in: int,
      c_out: int,
      input_size: int,
      h: int,
      patch_len: int,
      stride: int,
      max_seq_len: Optional[int] = 1024,
      n_layers: int = 3,
      hidden_size=128,
      n_heads=16,
      d_k: Optional[int] = None,
      d_v: Optional[int] = None,
      linear_hidden_size: int = 256,
      norm: str = "BatchNorm",
      attn_dropout: float = 0.0,
      dropout: float = 0.0,
      act: str = "gelu",
      key_padding_mask: str = "auto",
      padding_var: Optional[int] = None,
      attn_mask: Optional[torch.Tensor] = None,
      res_attention: bool = True,
      pre_norm: bool = False,
      store_attn: bool = False,
      pe: str = "zeros",
      learn_pe: bool = True,
      fc_dropout: float = 0.0,
      head_dropout=0,
      padding_patch=None,
      pretrain_head: bool = False,
      head_type="flatten",
      individual=False,
      revin=True,
      affine=True,
      subtract_last=False,
  ):
      super().__init__()
  
      # RevIn
      self.revin = revin
      if self.revin:
          self.revin_layer = RevIN(
              c_in, affine=affine, subtract_last=subtract_last
          )
  
      # Patching
      self.patch_len = patch_len
      self.stride = stride
      self.padding_patch = padding_patch
      patch_num = int((input_size - patch_len) / stride + 1)
      if padding_patch == "end":  # can be modified to general case
          self.padding_patch_layer = nn.ReplicationPad1d((0, stride))
          patch_num += 1
  
      # Backbone
      self.backbone = TSTiEncoder(
          c_in,
          patch_num=patch_num,
          patch_len=patch_len,
          max_seq_len=max_seq_len,
          n_layers=n_layers,
          hidden_size=hidden_size,
          n_heads=n_heads,
          d_k=d_k,
          d_v=d_v,
          linear_hidden_size=linear_hidden_size,
          attn_dropout=attn_dropout,
          dropout=dropout,
          act=act,
          key_padding_mask=key_padding_mask,
          padding_var=padding_var,
          attn_mask=attn_mask,
          res_attention=res_attention,
          pre_norm=pre_norm,
          store_attn=store_attn,
          pe=pe,
          learn_pe=learn_pe,
      )
  
      # Head
      self.head_nf = hidden_size * patch_num
      self.n_vars = c_in
      self.c_out = c_out
      self.pretrain_head = pretrain_head
      self.head_type = head_type
      self.individual = individual
  
      if self.pretrain_head:
          self.head = self.create_pretrain_head(
              self.head_nf, c_in, fc_dropout
          )  # custom head passed as a partial func with all its kwargs
      elif head_type == "flatten":
          self.head = Flatten_Head(
              self.individual,
              self.n_vars,
              self.head_nf,
              h,
              c_out,
              head_dropout=head_dropout,
          )
  
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 1 to create a new PatchTST_backbone object from inherited base classes: nn with example: obj = PatchTST_backbone(c_in, c_out, input_size, h, patch_len, stride, max_seq_len, n_layers, hidden_size, n_heads, d_k, d_v, linear_hidden_size, norm, attn_dropout, dropout, act, key_padding_mask, padding_var, attn_mask, res_attention, pre_norm, store_attn, pe, learn_pe, fc_dropout, head_dropout, padding_patch, pretrain_head, head_type, individual, revin, affine, subtract_last)