class_docstr: Model with Wavenet architecture and quantized target.
  
  Parameters
  ----------
  freq
      Frequency of the data to train on and predict
  prediction_length
      Length of the prediction horizon
  trainer
      Trainer object to be used (default: Trainer())
  cardinality
      Number of values of the each categorical feature (default: [1])
  embedding_dimension
      Dimension of the embeddings for categorical features (the same
      dimension is used for all embeddings, default: 5)
  num_bins
      Number of bins used for quantization of signal (default: 1024)
  hybridize_prediction_net
      Boolean (default: False)
  n_residue
      Number of residual channels in wavenet architecture (default: 24)
  n_skip
      Number of skip channels in wavenet architecture (default: 32)
  dilation_depth
      Number of dilation layers in wavenet architecture. If set to None
      (default), dialation_depth is set such that the receptive length is at
      least as long as typical seasonality for the frequency and at least
      2 * prediction_length.
  n_stacks
      Number of dilation stacks in wavenet architecture (default: 1)
  temperature
      Temparature used for sampling from softmax distribution. For
      temperature = 1.0 (default) sampling is according to estimated
      probability.
  act_type
      Activation type used after before output layer (default: "elu").
      Can be any of 'elu', 'relu', 'sigmoid', 'tanh', 'softrelu', 'softsign'.
  num_parallel_samples
      Number of evaluation samples per time series to increase parallelism
      during inference. This is a model optimization that does not affect the
      accuracy (default: 200)
  train_sampler
      Controls the sampling of windows during training.
  validation_sampler
      Controls the sampling of windows during validation.
  batch_size
      The size of the batches to be used training and prediction.
class_docstr_tok: ["Model","with","Wavenet","architecture","and","quantized","target",".","Parameters","--","--","--","--","--","freq","Frequency","of","the","data","to","train","on","and","predict","prediction_length","Length","of","the","prediction","horizon","trainer","Trainer","object","to","be","used","(","default",":","Trainer","(",")",")","cardinality","Number","of","values","of","the","each","categorical","feature","(","default",":","[","1","]",")","embedding_dimension","Dimension","of","the","embeddings","for","categorical","features","(","the","same","dimension","is","used","for","all","embeddings",",","default",":","5",")","num_bins","Number","of","bins","used","for","quantization","of","signal","(","default",":","1024",")","hybridize_prediction_net","Boolean","(","default",":","False",")","n_residue","Number","of","residual","channels","in","wavenet","architecture","(","default",":","24",")","n_skip","Number","of","skip","channels","in","wavenet","architecture","(","default",":","32",")","dilation_depth","Number","of","dilation","layers","in","wavenet","architecture",".","If","set","to","None","(","default",")",",","dialation_depth","is","set","such","that","the","receptive","length","is","at","least","as","long","as","typical","seasonality","for","the","frequency","and","at","least","2","*","prediction_length",".","n_stacks","Number","of","dilation","stacks","in","wavenet","architecture","(","default",":","1",")","temperature","Temparature","used","for","sampling","from","softmax","distribution",".","For","temperature","=","1.0","(","default",")","sampling","is","according","to","estimated","probability",".","act_type","Activation","type","used","after","before","output","layer","(","default",":","``","elu","''",")",".","Can","be","any","of","'elu","'",",","'relu","'",",","'sigmoid","'",",","'tanh","'",",","'softrelu","'",",","'softsign","'",".","num_parallel_samples","Number","of","evaluation","samples","per","time","series","to","increase","parallelism","during","inference",".","This","is","a","model","optimization","that","does","not","affect","the","accuracy","(","default",":","200",")","train_sampler","Controls","the","sampling","of","windows","during","training",".","validation_sampler","Controls","the","sampling","of","windows","during","validation",".","batch_size","The","size","of","the","batches","to","be","used","training","and","prediction","."]
code: def create_training_network(self) -> WaveNetTraining:
      params = self._get_wavenet_args()
      params.update(pred_length=self.train_window_length)
      return WaveNetTraining(**params)
  
imports: import logging
  from functools import partial
  from typing import List, Optional
  import mxnet
  import numpy
  from gluonts import transform
  from gluonts.core.component import validated
  from gluonts.dataset.common import DataEntry, Dataset
  from gluonts.dataset.field_names import FieldName
  from gluonts.dataset.loader import DataLoader, TrainDataLoader, ValidationDataLoader
  from gluonts.model.predictor import Predictor
  from gluonts.mx.batchify import batchify
  from gluonts.mx.model.estimator import GluonEstimator
  from gluonts.mx.model.predictor import RepresentableBlockPredictor
  from gluonts.mx.trainer import Trainer
  from gluonts.mx.util import copy_parameters, get_hybrid_forward_input_names
  from gluonts.time_feature import get_seasonality, time_features_from_frequency_str
  from gluonts.transform import AddAgeFeature, AddObservedValuesIndicator, AddTimeFeatures, AsNumpyArray, Chain, ExpectedNumInstanceSampler, InstanceSampler, InstanceSplitter, SelectFields, SetFieldIfNotPresent, SimpleTransformation, TestSplitSampler, ValidationSplitSampler, VstackFeatures
  from ._network import WaveNet, WaveNetSampler, WaveNetTraining
label_desc: Use image node_id 7 for calling the WaveNetEstimator obj's underlying member method code with example usage: obj.create_training_network() and returns: WaveNetTraining