code: def __init__(
      self,
      context_length: int,
      prediction_length: int,
      variable_dim: int,
      embed_dim: int,
      num_heads: int,
      num_outputs: int,
      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,
  ):
      super().__init__()
  
      self.context_length = context_length
      self.prediction_length = prediction_length
      self.normalize_eps = 1e-5
  
      self.target_proj = nn.Linear(
          in_features=1, out_features=variable_dim
      )
  
      if d_past_feat_dynamic_real:
          self.past_feat_dynamic_proj = FeatureProjector(
              feature_dims=d_past_feat_dynamic_real,
              embedding_dims=[variable_dim]
              * len(d_past_feat_dynamic_real),
          )
      else:
          self.past_feat_dynamic_proj = None
  
      if c_past_feat_dynamic_cat:
          self.past_feat_dynamic_embed = FeatureEmbedder(
              cardinalities=c_past_feat_dynamic_cat,
              embedding_dims=[variable_dim]
              * len(c_past_feat_dynamic_cat),
          )
      else:
          self.past_feat_dynamic_embed = None
  
      if d_feat_dynamic_real:
          self.feat_dynamic_proj = FeatureProjector(
              feature_dims=d_feat_dynamic_real,
              embedding_dims=[variable_dim] * len(d_feat_dynamic_real),
          )
      else:
          self.feat_dynamic_proj = None
  
      if c_feat_dynamic_cat:
          self.feat_dynamic_embed = FeatureEmbedder(
              cardinalities=c_feat_dynamic_cat,
              embedding_dims=[variable_dim] * len(c_feat_dynamic_cat),
          )
      else:
          self.feat_dynamic_embed = None
  
      if d_feat_static_real:
          self.feat_static_proj = FeatureProjector(
              feature_dims=d_feat_static_real,
              embedding_dims=[variable_dim] * len(d_feat_static_real),
          )
      else:
          self.feat_static_proj = None
  
      if c_feat_static_cat:
          self.feat_static_embed = FeatureEmbedder(
              cardinalities=c_feat_static_cat,
              embedding_dims=[variable_dim] * len(c_feat_static_cat),
          )
      else:
          self.feat_static_embed = None
  
      n_feat_static = len(d_feat_static_real) + len(c_feat_static_cat)
      self.static_selector = VariableSelectionNetwork(
          d_hidden=variable_dim,
          n_vars=n_feat_static,
          dropout=dropout,
      )
  
      n_past_feat_dynamic = len(d_past_feat_dynamic_real) + len(
          c_past_feat_dynamic_cat
      )
      n_feat_dynamic = len(d_feat_dynamic_real) + len(
          c_feat_dynamic_cat
      )
      self.ctx_selector = VariableSelectionNetwork(
          d_hidden=variable_dim,
          n_vars=n_past_feat_dynamic + n_feat_dynamic + 1,
          add_static=True,
          dropout=dropout,
      )
  
      self.tgt_selector = VariableSelectionNetwork(
          d_hidden=variable_dim,
          n_vars=n_feat_dynamic,
          add_static=True,
          dropout=dropout,
      )
  
      self.selection = GatedResidualNetwork(
          d_hidden=variable_dim,
          dropout=dropout,
      )
  
      self.enrichment = GatedResidualNetwork(
          d_hidden=variable_dim,
          dropout=dropout,
      )
  
      self.state_h = GatedResidualNetwork(
          d_hidden=variable_dim,
          d_output=embed_dim,
          dropout=dropout,
      )
  
      self.state_c = GatedResidualNetwork(
          d_hidden=variable_dim,
          d_output=embed_dim,
          dropout=dropout,
      )
  
      self.temporal_encoder = TemporalFusionEncoder(
          d_input=variable_dim,
          d_hidden=embed_dim,
      )
      self.temporal_decoder = TemporalFusionDecoder(
          context_length=self.context_length,
          prediction_length=self.prediction_length,
          d_hidden=embed_dim,
          d_var=variable_dim,
          n_head=num_heads,
          dropout=dropout,
      )
  
      self.quantiles = sum(
          [
              [i / 10, 1.0 - i / 10]
              for i in range(1, (num_outputs + 1) // 2)
          ],
          [0.5],
      )
      self.output = QuantileOutput(
          input_size=embed_dim, quantiles=self.quantiles
      )
      self.output_proj = self.output.get_quantile_proj()
      self.loss = self.output.get_loss()
  
imports: from typing import List, Optional, Tuple, Union
  import numpy
  import torch
  import torch.nn
  from torch.distributions import Distribution
  from gluonts.core.component import validated
  from pts.model import weighted_average
  from .tft_modules import FeatureProjector, FeatureEmbedder, VariableSelectionNetwork, GatedResidualNetwork, TemporalFusionEncoder, TemporalFusionDecoder
  from .tft_output import QuantileOutput
label_desc: Use image node_id 1 to create a new TemporalFusionTransformerNetwork object from inherited base classes: nn with example: obj = TemporalFusionTransformerNetwork(context_length, prediction_length, variable_dim, embed_dim, num_heads, num_outputs, 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)