code: def forward(self, x: Tensor) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
      variable_ctx, sparse_weights = self.vsn(x)
  
      # Context vectors:
      # variable selection context
      # enrichment context
      # state_c context
      # state_h context
      cs, ce, ch, cc = tuple(m(variable_ctx) for m in self.context_grns)
  
      return cs, ce, ch, cc
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from torch.nn import LayerNorm
  import logging
  import warnings
  from typing import Tuple, Optional
  from ..losses.pytorch import MAE
  from ..common._base_windows import BaseWindows
label_desc: Use image node_id 2 for calling the StaticCovariateEncoder obj's underlying member method code with example usage: obj.forward(x) and returns: cs,  ce,  ch,  cc