code: def __init__(
      self,
      prediction_length: int,
      context_length: int,
      interval_distr_output: DistributionOutput,
      size_distr_output: DistributionOutput,
      num_cells: int,
      num_layers: int,
      dropout_rate: float = 0.1,
      **kwargs,
  ):
      super().__init__(**kwargs)
  
      self.prediction_length = prediction_length
      self.context_length = context_length
      self.interval_distr_output = interval_distr_output
      self.size_distr_output = size_distr_output
      self.num_cells = num_cells
      self.num_layers = num_layers
  
      with self.name_scope():
          self.rnn_cell = mx.gluon.rnn.LSTM(
              hidden_size=num_cells,
              num_layers=num_layers,
              layout="NTC",
              dropout=dropout_rate,
              h2h_weight_initializer=mx.init.Xavier(),
              h2r_weight_initializer=mx.init.Xavier(),
          )
          self.dense = mx.gluon.nn.Dense(
              units=2,
              activation="softrelu",
              flatten=False,
              weight_initializer=mx.init.Xavier(),
              bias_initializer=mx.init.Constant(5.0),
          )
  
      # add constant learnable parameters if the distributions require
      # more than the conditional mean
      with self.name_scope():
          if len(self.interval_distr_output.args_dim) > 1:
              self.interval_alpha_bias = self.params.get(
                  "interval_alpha_bias",
                  shape=(1,),
                  init=mx.init.Constant(2),
              )
          if len(self.size_distr_output.args_dim) > 1:
              self.size_alpha_bias = self.params.get(
                  "size_alpha_bias",
                  shape=(1,),
                  init=mx.init.Constant(2),
              )
  
imports: from typing import Optional, Tuple
  import mxnet
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from gluonts.mx.distribution import Distribution, DistributionOutput
  from gluonts.mx.distribution.distribution import getF
  from mxnet import gluon
label_desc: Use image node_id 1 to create a new DeepRenewalNetwork object from inherited base classes: gluon with example: obj = DeepRenewalNetwork(prediction_length, context_length, interval_distr_output, size_distr_output, num_cells, num_layers, dropout_rate)