code: def __init__(
      self,
      bin_values: List[float],
      n_residue: int,
      n_skip: int,
      dilation_depth: int,
      n_stacks: int,
      act_type: str,
      cardinality: List[int],
      embedding_dimension: int,
      pred_length: int,
      **kwargs,
  ):
      super().__init__(**kwargs)
  
      self.dilation_depth = dilation_depth
      self.pred_length = pred_length
  
      self.mu = len(bin_values)
      self.dilations = WaveNet._get_dilations(
          dilation_depth=dilation_depth, n_stacks=n_stacks
      )
      self.receptive_field = WaveNet.get_receptive_field(
          dilation_depth=dilation_depth, n_stacks=n_stacks
      )
      self.trim_lengths = [
          sum(self.dilations) - sum(self.dilations[: i + 1])
          for i, _ in enumerate(self.dilations)
      ]
  
      with self.name_scope():
          self.feature_embedder = FeatureEmbedder(
              cardinalities=cardinality,
              embedding_dims=[embedding_dimension for _ in cardinality],
          )
  
          # self.post_transform = LookupValues(mx.nd.array(bin_values))
          self.target_embed = nn.Embedding(
              input_dim=self.mu, output_dim=n_residue
          )
          self.residuals = nn.HybridSequential()
          for i, d in enumerate(self.dilations):
              is_not_last = i + 1 < len(self.dilations)
              self.residuals.add(
                  CausalDilatedResidue(
                      n_residue=n_residue,
                      n_skip=n_skip,
                      dilation=d,
                      return_dense_out=is_not_last,
                      kernel_size=2,
                  )
              )
  
          std = 1.0 / math.sqrt(n_residue)
          self.conv_project = nn.Conv1D(
              channels=n_residue,
              kernel_size=1,
              use_bias=True,
              weight_initializer=mx.init.Uniform(std),
              bias_initializer="zero",
          )
  
          self.conv1 = conv1d(
              in_channels=n_skip, channels=n_skip, kernel_size=1
          )
  
          self.conv2 = conv1d(
              in_channels=n_skip, channels=self.mu, kernel_size=1
          )
          self.output_act = (
              nn.ELU()
              if act_type == "elu"
              else nn.Activation(activation=act_type)
          )
          self.cross_entropy_loss = gluon.loss.SoftmaxCrossEntropyLoss()
  
imports: import math
  from typing import List, Optional
  import mxnet
  from mxnet import gluon
  from mxnet.gluon import nn
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from gluonts.mx.block.feature import FeatureEmbedder
label_desc: Use image node_id 1 to create a new WaveNet object from inherited base classes: nn with example: obj = WaveNet(bin_values, n_residue, n_skip, dilation_depth, n_stacks, act_type, cardinality, embedding_dimension, pred_length)