code: def forward(self, x: Tensor, context: Optional[Tensor] = None):
      Xi = x.reshape(*x.shape[:-2], -1)
      grn_outputs = self.joint_grn(Xi, c=context)
      sparse_weights = F.softmax(grn_outputs, dim=-1)
      transformed_embed_list = [
          m(x[..., i, :]) for i, m in enumerate(self.var_grns)
      ]
      transformed_embed = torch.stack(transformed_embed_list, dim=-1)
      # the line below performs batched matrix vector multiplication
      # for temporal features it's bthf,btf->bth
      # for static features it's bhf,bf->bh
      variable_ctx = torch.matmul(
          transformed_embed, sparse_weights.unsqueeze(-1)
      ).squeeze(-1)
  
      return variable_ctx, sparse_weights
  
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 VariableSelectionNetwork obj's underlying member method code with example usage: obj.forward(x, context) and returns: variable_ctx,  sparse_weights