code: def wavenet(
      x,
      filters=32,
      skipfilters=512,
      quantization=256,
      blocks=10,
      repeats=5,
      is_training=False,
      scope=None,
      reuse=None,
  ):
      x = one_hot(x, quantization, name="one_hot")
      x = tf.pad(x, [[0, 0], [1, 0], [0, 0]])
      x = conv1d(
          x, filters, 2, biases_initializer=None, scope="embedding"
      )
  
      skips = []
      for i in range(blocks * repeats):
          x, s = block(
              x, filters, skipfilters, 2 ** (i % blocks), scope=str(i)
          )
          skips.append(s)
  
      x = relu(sum(skips), name="skips")
      x = conv1d(x, skipfilters, 1, scope="fc")
      x = relu(x, name="fc/relu")
      x = conv1d(x, quantization, 1, scope="logits")
      x = softmax(x, name="probs")
      return x
  
file_path: tensornets/tensornets/wavenets.py
imports: from __future__ import absolute_import
  from __future__ import division
  import tensorflow
  from .layers import conv1d
  from .ops import *
  from .utils import pad_info
  from .utils import set_args
  from .utils import var_scope
label_desc: Use image node_id 3 for calling a global function with example usage: wavenet(x, filters, skipfilters, quantization, blocks, repeats, is_training, scope, reuse) and returns: x