code: def hybrid_forward(self, F, features: Tensor) -> List[Tensor]:
      concat_features = super().hybrid_forward(F, features)
      if self.__num_features > 1:
          features = F.split(
              concat_features, num_outputs=self.__num_features, axis=-1
          )
      else:
          features = [concat_features]
      return features
  
imports: from typing import List, Tuple, Type
  import numpy
  from mxnet.gluon import HybridBlock, nn
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from gluonts.mx.block.feature import FeatureEmbedder
  from gluonts.mx.block.quantile_output import QuantileOutput
  from gluonts.mx.util import weighted_average
  from ._layers import GatedResidualNetwork, TemporalFusionDecoder, TemporalFusionEncoder, VariableSelectionNetwork
label_desc: Use image node_id 1 for calling the FeatureEmbedder obj's underlying member method code with example usage: obj.hybrid_forward(F, features) and returns: features