code: def __init__(
      self,
      input_size: int,
      num_layers: int,
      num_cells: int,
      cell_type: str,
      history_length: int,
      context_length: int,
      prediction_length: int,
      distr_output: DistributionOutput,
      control_output: DistributionOutput,
      dropout_rate: float,
      cardinality: List[int],
      embedding_dimension: List[int],
      lags_seq: List[int],
      scaling: bool = True,
      dtype: np.dtype = np.float32,
  ) -> None:
      super().__init__()
      self.num_layers = num_layers
      self.num_cells = num_cells
      self.cell_type = cell_type
      self.history_length = history_length
      self.context_length = context_length
      self.prediction_length = prediction_length
      self.dropout_rate = dropout_rate
      self.cardinality = cardinality
      self.embedding_dimension = embedding_dimension
      self.num_cat = len(cardinality)
      self.scaling = scaling
      self.dtype = dtype
  
      self.lags_seq = lags_seq
  
      self.distr_output = distr_output
      self.control_output = control_output
  
      rnn = {"LSTM": nn.LSTM, "GRU": nn.GRU}[self.cell_type]
      self.rnn = rnn(
          input_size=input_size,
          hidden_size=num_cells,
          num_layers=num_layers,
          dropout=dropout_rate,
          batch_first=True,
      )
  
      self.target_shape = distr_output.event_shape
  
      self.proj_distr_args = distr_output.get_args_proj(num_cells + 1)
      self.proj_control_args = control_output.get_args_proj(num_cells)
  
      self.embedder = FeatureEmbedder(
          cardinalities=cardinality, embedding_dims=embedding_dimension
      )
  
      if scaling:
          self.scaler = MeanScaler(keepdim=True)
          self.control_scaler = NOPScaler(keepdim=True)
      else:
          self.scaler = NOPScaler(keepdim=True)
          self.control_scaler = NOPScaler(keepdim=True)
  
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 gluonts.torch.modules.distribution_output import DistributionOutput
  from pts.model import weighted_average
  from pts.modules import MeanScaler, NOPScaler, FeatureEmbedder
label_desc: Use image node_id 1 to create a new CausalDeepARNetwork object from inherited base classes: nn with example: obj = CausalDeepARNetwork(input_size, num_layers, num_cells, cell_type, history_length, context_length, prediction_length, distr_output, control_output, dropout_rate, cardinality, embedding_dimension, lags_seq, scaling, dtype)