code: def forward(
      self, x: Dict[str, torch.Tensor], context: torch.Tensor = None
  ):
      if self.num_inputs > 1:
          # transform single variables
          var_outputs = []
          weight_inputs = []
          for name in self.input_sizes.keys():
              # select embedding belonging to a single input
              variable_embedding = x[name]
              if name in self.prescalers:
                  variable_embedding = self.prescalers[name](
                      variable_embedding
                  )
              weight_inputs.append(variable_embedding)
              var_outputs.append(
                  self.single_variable_grns[name](variable_embedding)
              )
          var_outputs = torch.stack(var_outputs, dim=-1)
  
          # calculate variable weights
          flat_embedding = torch.cat(weight_inputs, dim=-1)
          sparse_weights = self.flattened_grn(flat_embedding, context)
          sparse_weights = self.softmax(sparse_weights).unsqueeze(-2)
  
          outputs = var_outputs * sparse_weights
          outputs = outputs.sum(dim=-1)
      else:  # for one input, do not perform variable selection but just encoding
          name = next(iter(self.single_variable_grns.keys()))
          variable_embedding = x[name]
          if name in self.prescalers:
              variable_embedding = self.prescalers[name](
                  variable_embedding
              )
          outputs = self.single_variable_grns[name](
              variable_embedding
          )  # fast forward if only one variable
          if (
              outputs.ndim == 3
          ):  # -> batch size, time, hidden size, n_variables
              sparse_weights = torch.ones(
                  outputs.size(0),
                  outputs.size(1),
                  1,
                  1,
                  device=outputs.device,
              )  #
          else:  # ndim == 2 -> batch size, hidden size, n_variables
              sparse_weights = torch.ones(
                  outputs.size(0), 1, 1, device=outputs.device
              )
      return outputs, sparse_weights
  
imports: from typing import Dict, List, Optional, Tuple, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from darts.logging import get_logger
  from darts.utils.torch import MonteCarloDropout
label_desc: Use image node_id 4 for calling the _VariableSelectionNetwork obj's underlying member method code with example usage: obj.forward(x, context) and returns: outputs,  sparse_weights