class_docstr: The WaveNet model.
  
  Parameters
  ----------
  pred_length
      Prediction length.
  bin_values
      List of bin values.
  num_residual_channels
      Number of residual channels.
  num_skip_channels
      Number of skip channels.
  dilation_depth
      The depth of the dilated convolution.
  num_stacks
      The number of dilation stacks.
  num_feat_dynamic_real, optional
      The number of dynamic real features, by default 1
  num_feat_static_real, optional
      The number of static real features, by default 1
  cardinality, optional
      The cardinalities of static categorical features, by default [1]
  embedding_dimension, optional
      The dimension of the embeddings for categorical features, by default 5
  num_parallel_samples, optional
      The number of parallel samples to generate during inference.
      This parameter is only used in inference mode, by default 100
  temperature, optional
      Temparature used for sampling from the output softmax distribution,
      by default 1.0
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docstr: Forward pass through the WaveNet.
  
  Parameters
  ----------
  inputs
      A tensor of inputs
      Shape: (batch_size, num_residual_channels, sequence_length)
  queues, optional
      Convolutional queues containing past computations.
      This speeds up predictions and must be provided
      during prediction mode. See [Paine et al., 2016] for details,
      by default None
  
  [Paine et al., 2016] "Fast wavenet generation algorithm."
     arXiv preprint arXiv:1611.09482 (2016).
  
  Returns
  -------
      A tensor containing the unnormalized outputs of the network of
      shape (batch_size, pred_length, num_bins) and a list containing the
      convolutional queues for each layer. The queue corresponding to
      layer `l` has shape: (batch_size, num_residual_channels, 2^l).
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code: def base_net(
      self,
      inputs: torch.Tensor,
      queues: Optional[List[torch.Tensor]] = None,
  ) -> Tuple[torch.Tensor, List[torch.Tensor]]:
      """Forward pass through the WaveNet.
  
      Parameters
      ----------
      inputs
          A tensor of inputs
          Shape: (batch_size, num_residual_channels, sequence_length)
      queues, optional
          Convolutional queues containing past computations.
          This speeds up predictions and must be provided
          during prediction mode. See [Paine et al., 2016] for details,
          by default None
  
      [Paine et al., 2016] "Fast wavenet generation algorithm."
         arXiv preprint arXiv:1611.09482 (2016).
  
      Returns
      -------
          A tensor containing the unnormalized outputs of the network of
          shape (batch_size, pred_length, num_bins) and a list containing the
          convolutional queues for each layer. The queue corresponding to
          layer `l` has shape: (batch_size, num_residual_channels, 2^l).
      """
      skip_outs = []
      queues_next = []
      out = inputs
      for i, layer in enumerate(self.residuals):
          skip, out = layer(out)
          if queues is not None:
              trimmed_skip = skip
              if i + 1 < len(self.residuals):
                  out = torch.cat([queues[i], out], dim=-1)
                  queues_next.append(out[..., 1:])
          else:
              trimmed_skip = skip[..., self.trim_lengths[i] :]
          skip_outs.append(trimmed_skip)
  
      y = torch.stack(skip_outs).sum(dim=0)
      y = self.output_act(y)
      y = self.conv1(y)
      y = self.output_act(y)
      y = self.conv2(y)
      logits = y.transpose(1, 2)
      return logits, queues_next
  
imports: from typing import List, Optional, Tuple
  import torch
  import torch.nn
  from gluonts.core.component import validated
  from gluonts.torch.modules.feature import FeatureEmbedder
  from gluonts.torch.modules.lookup_table import LookupValues
label_desc: Use image node_id 6 for calling the WaveNet obj's underlying member method code with example usage: obj.base_net(inputs, queues) and returns: logits,  queues_next