code: def forward(self, x):  # x: [bs x nvars x hidden_size x patch_num]
      if self.individual:
          x_out = []
          for i in range(self.n_vars):
              z = self.flattens[i](
                  x[:, i, :, :]
              )  # z: [bs x hidden_size * patch_num]
              z = self.linears[i](z)  # z: [bs x h]
              z = self.dropouts[i](z)
              x_out.append(z)
          x = torch.stack(x_out, dim=1)  # x: [bs x nvars x h]
      else:
          x = self.flatten(x)
          x = self.linear(x)
          x = self.dropout(x)
      return x
  
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 Flatten_Head obj's underlying member method code with example usage: obj.forward(x) and returns: x