code: def hybrid_forward(
      self,
      F,
      variables: List[Tensor],
      static: Optional[Tensor] = None,
  ) -> Tuple[Tensor, Tensor]:
      if len(variables) != self.n_vars:
          raise ValueError(
              f"expect {self.n_vars} variables, {len(variables)} given."
          )
      if self.add_static and static is None:
          raise ValueError("static variable is expected.")
      if not self.add_static and static is not None:
          raise ValueError("static variable is not accpeted.")
      flatten = F.concat(*variables, dim=-1)
      if static is not None:
          static = F.broadcast_like(static, variables[0])
      weight = self.weight_network(flatten, static)
      weight = F.expand_dims(weight, axis=-2)
      weight = F.softmax(weight, axis=-1)
      var_encodings = []
      for var, net in zip(variables, self.variable_network):
          var_encodings.append(net(var))
      var_encodings = F.stack(*var_encodings, axis=-1)
      var_encodings = F.sum(
          F.broadcast_mul(var_encodings, weight), axis=-1
      )
      return var_encodings, weight
  
imports: import math
  from typing import List, Optional, Tuple
  from mxnet import init
  from mxnet.gluon import HybridBlock, nn, rnn
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
label_desc: Use image node_id 2 for calling the VariableSelectionNetwork obj's underlying member method code with example usage: obj.hybrid_forward(F, variables, static) and returns: var_encodings,  weight