code: def __init__(
      self,
      context_length: int,
      prediction_length: int,
      d_var: int,
      d_hidden: int,
      n_head: int,
      quantiles: List[float],
      d_past_feat_dynamic_real: List[int],
      c_past_feat_dynamic_cat: List[int],
      d_feat_dynamic_real: List[int],
      c_feat_dynamic_cat: List[int],
      d_feat_static_real: List[int],
      c_feat_static_cat: List[int],
      dropout: float = 0.0,
      **kwargs,
  ):
      super().__init__(**kwargs)
      self.context_length = context_length
      self.prediction_length = prediction_length
      self.d_var = d_var
      self.d_hidden = d_hidden
      self.n_head = n_head
      self.quantiles = quantiles
      self.normalize_eps = 1e-5
  
      self.d_past_feat_dynamic_real = d_past_feat_dynamic_real
      self.c_past_feat_dynamic_cat = c_past_feat_dynamic_cat
      self.d_feat_dynamic_real = d_feat_dynamic_real
      self.c_feat_dynamic_cat = c_feat_dynamic_cat
      self.d_feat_static_real = d_feat_static_real
      self.c_feat_static_cat = c_feat_static_cat
      self.n_past_feat_dynamic = len(
          self.d_past_feat_dynamic_real
      ) + len(self.c_past_feat_dynamic_cat)
      self.n_feat_dynamic = len(self.d_feat_dynamic_real) + len(
          self.c_feat_dynamic_cat
      )
      self.n_feat_static = len(self.d_feat_static_real) + len(
          self.c_feat_static_cat
      )
  
      with self.name_scope():
          self.target_proj = nn.Dense(
              units=self.d_var,
              in_units=1,
              flatten=False,
              prefix="target_projection_",
          )
          if self.d_past_feat_dynamic_real:
              self.past_feat_dynamic_proj = FeatureProjector(
                  feature_dims=self.d_past_feat_dynamic_real,
                  embedding_dims=[self.d_var]
                  * len(self.d_past_feat_dynamic_real),
                  prefix="past_feat_dynamic_",
              )
          else:
              self.past_feat_dynamic_proj = None
  
          if self.c_past_feat_dynamic_cat:
              self.past_feat_dynamic_embed = FeatureEmbedder(
                  cardinalities=self.c_past_feat_dynamic_cat,
                  embedding_dims=[self.d_var]
                  * len(self.c_past_feat_dynamic_cat),
                  prefix="past_feat_dynamic_",
              )
          else:
              self.past_feat_dynamic_embed = None
  
          if self.d_feat_dynamic_real:
              self.feat_dynamic_proj = FeatureProjector(
                  feature_dims=self.d_feat_dynamic_real,
                  embedding_dims=[self.d_var]
                  * len(self.d_feat_dynamic_real),
                  prefix="feat_dynamic_",
              )
          else:
              self.feat_dynamic_proj = None
  
          if self.c_feat_dynamic_cat:
              self.feat_dynamic_embed = FeatureEmbedder(
                  cardinalities=self.c_feat_dynamic_cat,
                  embedding_dims=[self.d_var]
                  * len(self.c_feat_dynamic_cat),
                  prefix="feat_dynamic_",
              )
          else:
              self.feat_dynamic_embed = None
  
          if self.d_feat_static_real:
              self.feat_static_proj = FeatureProjector(
                  feature_dims=self.d_feat_static_real,
                  embedding_dims=[self.d_var]
                  * len(self.d_feat_static_real),
                  prefix="feat_static_",
              )
          else:
              self.feat_static_proj = None
  
          if self.c_feat_static_cat:
              self.feat_static_embed = FeatureEmbedder(
                  cardinalities=self.c_feat_static_cat,
                  embedding_dims=[self.d_var]
                  * len(self.c_feat_static_cat),
                  prefix="feat_static_",
              )
          else:
              self.feat_static_embed = None
  
          self.static_selector = VariableSelectionNetwork(
              d_hidden=self.d_var,
              n_vars=self.n_feat_static,
              dropout=dropout,
          )
          self.ctx_selector = VariableSelectionNetwork(
              d_hidden=self.d_var,
              n_vars=self.n_past_feat_dynamic + self.n_feat_dynamic + 1,
              add_static=True,
              dropout=dropout,
          )
          self.tgt_selector = VariableSelectionNetwork(
              d_hidden=self.d_var,
              n_vars=self.n_feat_dynamic,
              add_static=True,
              dropout=dropout,
          )
          self.selection = GatedResidualNetwork(
              d_hidden=self.d_var,
              dropout=dropout,
          )
          self.enrichment = GatedResidualNetwork(
              d_hidden=self.d_var,
              dropout=dropout,
          )
          self.state_h = GatedResidualNetwork(
              d_hidden=self.d_var,
              d_output=self.d_hidden,
              dropout=dropout,
          )
          self.state_c = GatedResidualNetwork(
              d_hidden=self.d_var,
              d_output=self.d_hidden,
              dropout=dropout,
          )
          self.temporal_encoder = TemporalFusionEncoder(
              context_length=self.context_length,
              prediction_length=self.prediction_length,
              d_input=self.d_var,
              d_hidden=self.d_hidden,
          )
          self.temporal_decoder = TemporalFusionDecoder(
              context_length=self.context_length,
              prediction_length=self.prediction_length,
              d_hidden=self.d_hidden,
              d_var=self.d_var,
              n_head=self.n_head,
              dropout=dropout,
          )
          self.output = QuantileOutput(quantiles=self.quantiles)
          self.output_proj = self.output.get_quantile_proj()
          self.loss = self.output.get_loss()
  
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 1 to create a new TemporalFusionTransformerNetwork object from inherited base classes: HybridBlock with example: obj = TemporalFusionTransformerNetwork(context_length, prediction_length, d_var, d_hidden, n_head, quantiles, d_past_feat_dynamic_real, c_past_feat_dynamic_cat, d_feat_dynamic_real, c_feat_dynamic_cat, d_feat_static_real, c_feat_static_cat, dropout)