code: def _preprocess(
      self,
      F,
      past_target: Tensor,
      past_observed_values: Tensor,
      past_feat_dynamic_real: Tensor,
      past_feat_dynamic_cat: Tensor,
      feat_dynamic_real: Tensor,
      feat_dynamic_cat: Tensor,
      feat_static_real: Tensor,
      feat_static_cat: Tensor,
  ):
      obs = F.broadcast_mul(past_target, past_observed_values)
      count = F.sum(past_observed_values, axis=1, keepdims=True)
      offset = F.broadcast_div(
          F.sum(obs, axis=1, keepdims=True),
          count + self.normalize_eps,
      )
      scale = F.broadcast_div(
          F.sum(obs**2, axis=1, keepdims=True),
          count + self.normalize_eps,
      )
      scale = F.broadcast_sub(scale, offset**2)
      scale = F.sqrt(scale)
      past_target = F.broadcast_div(
          F.broadcast_sub(past_target, offset),
          scale + self.normalize_eps,
      )
      past_target = F.expand_dims(past_target, axis=-1)
  
      past_covariates = []
      future_covariates = []
      static_covariates: List[Tensor] = []
      proj = self.target_proj(past_target)
      past_covariates.append(proj)
      if self.past_feat_dynamic_proj is not None:
          projs = self.past_feat_dynamic_proj(past_feat_dynamic_real)
          past_covariates.extend(projs)
      if self.past_feat_dynamic_embed is not None:
          embs = self.past_feat_dynamic_embed(past_feat_dynamic_cat)
          past_covariates.extend(embs)
      if self.feat_dynamic_proj is not None:
          projs = self.feat_dynamic_proj(feat_dynamic_real)
          for proj in projs:
              ctx_proj = F.slice_axis(
                  proj, axis=1, begin=0, end=self.context_length
              )
              tgt_proj = F.slice_axis(
                  proj, axis=1, begin=self.context_length, end=None
              )
              past_covariates.append(ctx_proj)
              future_covariates.append(tgt_proj)
      if self.feat_dynamic_embed is not None:
          embs = self.feat_dynamic_embed(feat_dynamic_cat)
          for emb in embs:
              ctx_emb = F.slice_axis(
                  emb, axis=1, begin=0, end=self.context_length
              )
              tgt_emb = F.slice_axis(
                  emb, axis=1, begin=self.context_length, end=None
              )
              past_covariates.append(ctx_emb)
              future_covariates.append(tgt_emb)
  
      if self.feat_static_proj is not None:
          projs = self.feat_static_proj(feat_static_real)
          static_covariates.extend(projs)
      if self.feat_static_embed is not None:
          embs = self.feat_static_embed(feat_static_cat)
          static_covariates.extend(embs)
  
      return (
          past_covariates,
          future_covariates,
          static_covariates,
          offset,
          scale,
      )
  
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 2 for calling the TemporalFusionTransformerNetwork obj's underlying member method code with example usage: obj._preprocess(F, past_target, past_observed_values, past_feat_dynamic_real, past_feat_dynamic_cat, feat_dynamic_real, feat_dynamic_cat, feat_static_real, feat_static_cat) and returns: past_covariates,  future_covariates,  static_covariates,  offset,  scale