code: def _preprocess(
      self,
      F,
      past_target: Tensor,
      past_observed_values: Tensor,
      past_is_pad: Tensor,
      past_feat_dynamic_real: Tensor,
      past_feat_dynamic_cat: Tensor,
      future_target: Tensor,
      future_feat_dynamic_real: Tensor,
      future_feat_dynamic_cat: Tensor,
      feat_static_real: Tensor,
      feat_static_cat: Tensor,
  ) -> Tuple[
      Tensor,
      Optional[Tensor],
      Tensor,
      Tensor,
      Optional[Tensor],
      Tensor,
      Tensor,
  ]:
      obs = past_target * past_observed_values
      count = F.sum(past_observed_values, axis=1, keepdims=True)
      offset = F.sum(obs, axis=1, keepdims=True) / (
          count + self.normalizer_eps
      )
      scale = F.sum(obs**2, axis=1, keepdims=True) / (
          count + self.normalizer_eps
      )
      scale = scale - offset**2
      scale = scale.sqrt()
  
      past_target = (past_target - offset) / (
          scale + self.normalizer_eps
      )
      if future_target is not None:
          future_target = (future_target - offset) / (
              scale + self.normalizer_eps
          )
  
      def _assemble_covariates(
          feat_dynamic_real: Tensor,
          feat_dynamic_cat: Tensor,
          feat_static_real: Tensor,
          feat_static_cat: Tensor,
          is_past: bool,
      ) -> Tensor:
          covariates = []
          if feat_dynamic_real.shape[-1] > 0:
              covariates.append(feat_dynamic_real)
          if feat_static_real.shape[-1] > 0:
              covariates.append(
                  feat_static_real.expand_dims(axis=1).repeat(
                      axis=1,
                      repeats=self.context_length
                      if is_past
                      else self.prediction_length,
                  )
              )
          if len(covariates) > 0:
              covariates = F.concat(*covariates, dim=-1)
              covariates = self.covar_proj(covariates)
          else:
              covariates = None
  
          categories = []
          if feat_dynamic_cat.shape[-1] > 0:
              categories.append(feat_dynamic_cat)
          if feat_static_cat.shape[-1] > 0:
              categories.append(
                  feat_static_cat.expand_dims(axis=1).repeat(
                      axis=1,
                      repeats=self.context_length
                      if is_past
                      else self.prediction_length,
                  )
              )
          if len(categories) > 0:
              categories = F.concat(*categories, dim=-1)
              embeddings = self.embedder(categories)
              embeddings = F.reshape(
                  embeddings, shape=(0, 0, -4, self.d_hidden, -1)
              ).sum(axis=-1)
              if covariates is not None:
                  covariates = covariates + embeddings
              else:
                  covariates = embeddings
          else:
              pass
  
          return covariates
  
      past_covariates = _assemble_covariates(
          past_feat_dynamic_real,
          past_feat_dynamic_cat,
          feat_static_real,
          feat_static_cat,
          is_past=True,
      )
      future_covariates = _assemble_covariates(
          future_feat_dynamic_real,
          future_feat_dynamic_cat,
          feat_static_real,
          feat_static_cat,
          is_past=False,
      )
      past_observed_values = F.broadcast_logical_and(
          past_observed_values,
          F.logical_not(past_is_pad),
      )
  
      return (
          past_target,
          past_covariates,
          past_observed_values,
          future_target,
          future_covariates,
          offset,
          scale,
      )
  
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 2 for calling the SelfAttentionNetwork obj's underlying member method code with example usage: obj._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) and returns: past_target,  past_covariates,  past_observed_values,  future_target,  future_covariates,  offset,  scale, covariates