code: def hybrid_forward(
      self,
      F,
      past_target: Tensor,
      past_observed_values: Tensor,
      past_is_pad: Tensor,
      future_target: Tensor,
      future_observed_values: Tensor,
      past_feat_dynamic_real: Tensor,
      past_feat_dynamic_cat: Tensor,
      future_feat_dynamic_real: Tensor,
      future_feat_dynamic_cat: Tensor,
      feat_static_real: Tensor,
      feat_static_cat: Tensor,
  ) -> Tensor:
      (
          past_target,
          past_covariates,
          past_observed_values,
          future_target,
          future_covariates,
          offset,
          scale,
      ) = self._preprocess(
          F,
          past_target,
          past_observed_values,
          past_is_pad,
          past_feat_dynamic_real,
          past_feat_dynamic_cat,
          future_target,
          future_feat_dynamic_real,
          future_feat_dynamic_cat,
          feat_static_real,
          feat_static_cat,
      )
  
      target = F.concat(past_target, future_target, dim=1)
      covars = F.concat(past_covariates, future_covariates, dim=1)
      observed_values = F.concat(
          past_observed_values, future_observed_values, dim=1
      )
  
      target = F.slice_axis(target, axis=1, begin=0, end=-1)
      covars = F.slice_axis(covars, axis=1, begin=0, end=-1)
      observed_values = F.slice_axis(
          observed_values, axis=1, begin=0, end=-1
      )
  
      preds = self._forward_step(
          F, self.prediction_length, target, covars, observed_values
      )
      preds = self._postprocess(F, preds, offset, scale)
      future_target = (
          future_target * (scale + self.normalizer_eps) + offset
      )
      loss = self.loss(future_target, preds)
      loss = weighted_average(F, loss, future_observed_values)
      return loss.mean()
  
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 1 for calling the SelfAttentionTrainingNetwork obj's underlying member method code with example usage: obj.hybrid_forward(F, past_target, past_observed_values, past_is_pad, future_target, future_observed_values, past_feat_dynamic_real, past_feat_dynamic_cat, future_feat_dynamic_real, future_feat_dynamic_cat, feat_static_real, feat_static_cat) and returns: loss