code: def _forward_step(
      self,
      F,
      horizon: int,
      target: Tensor,
      covars: Optional[Tensor],
      mask: Tensor,
  ) -> Tensor:
      target = F.expand_dims(target, axis=-1)
      mask = F.expand_dims(mask, axis=-1)
      value = self.target_proj(target)
      if covars is not None:
          value = value + covars
      for block in self._blocks:
          value = block(value, mask)
      value = F.slice_axis(value, axis=1, begin=-horizon, end=None)
      preds = self.output_proj(value)
      return preds
  
imports: from typing import List, Optional, Tuple
  from mxnet import init
  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 PosFFN, SelfAttention
label_desc: Use image node_id 4 for calling the SelfAttentionNetwork obj's underlying member method code with example usage: obj._forward_step(F, horizon, target, covars, mask) and returns: preds