docstr: Forward pass through the network.
  
  Parameters
  ----------
  F
  inputs
      Inputs to the network: (batch_size, n_residue, sequence_length)
  one_step_prediction
      Flag indicating whether the network is "unrolled/propagated" one
      step at a time (prediction phase).
  queues
      Convolutional queues containing past computations. Should be
      provided if `one_step_prediction` is True.
  
  Returns
  -------
  Tuple: (Tensor, List)
      A tensor containing the unnormalized outputs of the network. Shape:
      (batch_size, pred_length, num_bins). A list containing the
      convolutional queues for each layer. The queue corresponding to
      layer `l` has shape: (batch_size, n_residue, 2^l).
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code: def base_net(self, F, inputs, one_step_prediction=False, queues=None):
      """
      Forward pass through the network.
  
      Parameters
      ----------
      F
      inputs
          Inputs to the network: (batch_size, n_residue, sequence_length)
      one_step_prediction
          Flag indicating whether the network is "unrolled/propagated" one
          step at a time (prediction phase).
      queues
          Convolutional queues containing past computations. Should be
          provided if `one_step_prediction` is True.
  
      Returns
      -------
      Tuple: (Tensor, List)
          A tensor containing the unnormalized outputs of the network. Shape:
          (batch_size, pred_length, num_bins). A list containing the
          convolutional queues for each layer. The queue corresponding to
          layer `l` has shape: (batch_size, n_residue, 2^l).
      """
      if one_step_prediction:
          assert (
              queues is not None
          ), "Queues must not be empty during prediction phase!"
      skip_outs = []
      queues_next = []
      o = inputs
      for i, d in enumerate(self.dilations):
          skip, o = self.residuals[i](o)
          if one_step_prediction:
              skip_trim = skip
              if not self.is_last_layer(i):
                  q = queues[i]
                  o = F.concat(q, o, num_args=2, dim=-1)
                  queues_next.append(
                      F.slice_axis(o, begin=1, end=None, axis=-1)
                  )
          else:
              skip_trim = F.slice_axis(
                  skip, begin=self.trim_lengths[i], end=None, axis=-1
              )
          skip_outs.append(skip_trim)
      y = sum(skip_outs)
      y = self.output_act(y)
      y = self.conv1(y)
      y = self.output_act(y)
      y = self.conv2(y)
      unnormalized_output = y.swapaxes(1, 2)
      return unnormalized_output, queues_next
  
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 7 for calling the WaveNet obj's underlying member method code with example usage: obj.base_net(F, inputs, one_step_prediction, queues) and returns: unnormalized_output,  queues_next