code: def hybrid_forward(
      self,
      F,
      past_target: Tensor,
      past_observed_values: Tensor,
      past_is_pad: 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_covariates,
          offset,
          scale,
      ) = self._preprocess(
          F,
          past_target,
          past_observed_values,
          past_is_pad,
          past_feat_dynamic_real,
          past_feat_dynamic_cat,
          None,
          future_feat_dynamic_real,
          future_feat_dynamic_cat,
          feat_static_real,
          feat_static_cat,
      )
  
      target = past_target
      covars = past_covariates
      observed_values = past_observed_values
  
      preds = []
      for step in range(self.prediction_length):
          forecast = self._forward_step(
              F, 1, target, covars, observed_values
          )
          preds.append(forecast)
          next_target = F.slice_axis(forecast, axis=-1, begin=0, end=1)
          next_target = F.squeeze(next_target, axis=-1)
          next_covars = F.slice_axis(
              future_covariates, axis=1, begin=step, end=step + 1
          )
          next_observed_value = F.ones_like(next_target)
  
          target = F.concat(target, next_target, dim=1)
          covars = F.concat(covars, next_covars, dim=1)
          observed_values = F.concat(
              observed_values, next_observed_value, dim=1
          )
      preds = F.concat(*preds, dim=1)
      preds = self._postprocess(F, preds, offset, scale)
      preds = F.swapaxes(preds, dim1=1, dim2=2)
      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 1 for calling the SelfAttentionPredictionNetwork obj's underlying member method code with example usage: obj.hybrid_forward(F, past_target, past_observed_values, past_is_pad, past_feat_dynamic_real, past_feat_dynamic_cat, future_feat_dynamic_real, future_feat_dynamic_cat, feat_static_real, feat_static_cat) and returns: preds