code: def forward(
      self, x
  ) -> torch.Tensor:  # x: [bs x nvars x patch_len x patch_num]
      n_vars = x.shape[1]
      # Input encoding
      x = x.permute(
          0, 1, 3, 2
      )  # x: [bs x nvars x patch_num x patch_len]
      x = self.W_P(x)  # x: [bs x nvars x patch_num x hidden_size]
  
      u = torch.reshape(
          x, (x.shape[0] * x.shape[1], x.shape[2], x.shape[3])
      )  # u: [bs * nvars x patch_num x hidden_size]
      u = self.dropout(
          u + self.W_pos
      )  # u: [bs * nvars x patch_num x hidden_size]
  
      # Encoder
      z = self.encoder(u)  # z: [bs * nvars x patch_num x hidden_size]
      z = torch.reshape(
          z, (-1, n_vars, z.shape[-2], z.shape[-1])
      )  # z: [bs x nvars x patch_num x hidden_size]
      z = z.permute(
          0, 1, 3, 2
      )  # z: [bs x nvars x hidden_size x patch_num]
  
      return z
  
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 2 for calling the TSTiEncoder obj's underlying member method code with example usage: obj.forward(x) and returns: z