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: Initialize the convolutional queues to speed up predictions.
  
  Parameters
  ----------
  past_target
      Past target: (batch_size, receptive_field)
  features
      Tensor of features: (batch_size, num_features, receptive_field)
  
  Returns
  -------
      A list containing the convolutional queues for each layer.
      The queue corresponding to layer `l` has shape:
      (batch_size, n_residue, 2^l).
docstr_tok: ["Initialize","the","convolutional","queues","to","speed","up","predictions",".","Parameters","--","--","--","--","--","past_target","Past","target",":","(","batch_size",",","receptive_field",")","features","Tensor","of","features",":","(","batch_size",",","num_features",",","receptive_field",")","Returns","--","--","--","-","A","list","containing","the","convolutional","queues","for","each","layer",".","The","queue","corresponding","to","layer","`","l","`","has","shape",":","(","batch_size",",","n_residue",",","2^l",")","."]
code: def _initialize_conv_queues(
      self, past_target: torch.Tensor, features: torch.Tensor
  ) -> List[torch.Tensor]:
      """Initialize the convolutional queues to speed up predictions.
  
      Parameters
      ----------
      past_target
          Past target: (batch_size, receptive_field)
      features
          Tensor of features: (batch_size, num_features, receptive_field)
  
      Returns
      -------
          A list containing the convolutional queues for each layer.
          The queue corresponding to layer `l` has shape:
          (batch_size, n_residue, 2^l).
      """
      out = self.target_feature_embedding(past_target, features)
      queues = []
      for i, (d, layer) in enumerate(
          zip(self.dilations, self.residuals)
      ):
          sz = 1 if d == 2 ** (self.dilation_depth - 1) else d * 2
          _, out = layer(out)
  
          if i + 1 < len(self.dilations):
              out_chunk = out[..., -sz - 1 : -1]
          else:
              out_chunk = out
          queues.append(out_chunk)
      return queues
  
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 8 for calling the WaveNet obj's underlying member method code with example usage: obj._initialize_conv_queues(past_target, features) and returns: queues