code: def __init__(
      self,
      num_layers: int,
      num_cells: int,
      cell_type: str,
      past_length: int,
      prediction_length: int,
      issm: ISSM,
      dropout_rate: float,
      cardinality: List[int],
      embedding_dimension: List[int],
      scaling: bool = True,
      noise_std_bounds: ParameterBounds = ParameterBounds(1e-6, 1.0),
      prior_cov_bounds: ParameterBounds = ParameterBounds(1e-6, 1.0),
      innovation_bounds: ParameterBounds = ParameterBounds(1e-6, 0.01),
      **kwargs,
  ) -> None:
      super().__init__(**kwargs)
      self.num_layers = num_layers
      self.num_cells = num_cells
      self.cell_type = cell_type
      self.past_length = past_length
      self.prediction_length = prediction_length
      self.issm = issm
      self.dropout_rate = dropout_rate
      self.cardinality = cardinality
      self.embedding_dimension = embedding_dimension
      self.num_cat = len(cardinality)
      self.scaling = scaling
  
      assert len(cardinality) == len(embedding_dimension), (
          "embedding_dimension should be a list with the same size as"
          " cardinality"
      )
      self.univariate = self.issm.output_dim() == 1
  
      self.noise_std_bounds = noise_std_bounds
      self.prior_cov_bounds = prior_cov_bounds
      self.innovation_bounds = innovation_bounds
  
      with self.name_scope():
          self.prior_mean_model = mx.gluon.nn.Dense(
              units=self.issm.latent_dim(), flatten=False
          )
          self.prior_cov_diag_model = mx.gluon.nn.Dense(
              units=self.issm.latent_dim(),
              activation="sigmoid",
              flatten=False,
          )
          self.lstm = mx.gluon.rnn.HybridSequentialRNNCell()
          self.lds_proj = LDSArgsProj(
              output_dim=self.issm.output_dim(),
              noise_std_bounds=self.noise_std_bounds,
              innovation_bounds=self.innovation_bounds,
          )
          for k in range(num_layers):
              cell = mx.gluon.rnn.LSTMCell(hidden_size=num_cells)
              cell = mx.gluon.rnn.ResidualCell(cell) if k > 0 else cell
              cell = (
                  mx.gluon.rnn.ZoneoutCell(
                      cell, zoneout_states=dropout_rate
                  )
                  if dropout_rate > 0.0
                  else cell
              )
              self.lstm.add(cell)
          self.embedder = FeatureEmbedder(
              cardinalities=cardinality,
              embedding_dims=embedding_dimension,
          )
          if scaling:
              self.scaler = MeanScaler(keepdims=False)
          else:
              self.scaler = NOPScaler(keepdims=False)
  
imports: from typing import List, Optional
  import mxnet
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from gluonts.mx.block.feature import FeatureEmbedder
  from gluonts.mx.block.scaler import MeanScaler, NOPScaler
  from gluonts.mx.distribution.lds import LDS, LDSArgsProj, ParameterBounds
  from gluonts.mx.util import make_nd_diag, weighted_average
  from .issm import ISSM
label_desc: Use image node_id 1 to create a new DeepStateNetwork object from inherited base classes: mx.gluon with example: obj = DeepStateNetwork(num_layers, num_cells, cell_type, past_length, prediction_length, issm, dropout_rate, cardinality, embedding_dimension, scaling, noise_std_bounds, prior_cov_bounds, innovation_bounds)