code: def causal_linear(
      x, n_inputs, n_outputs, name, filter_length, rate, batch_size
  ):
      """Applies dilated convolution using queues.
  
      Assumes a filter_length of 3.
  
      Args:
        x: The [mb, time, channels] tensor input.
        n_inputs: The input number of channels.
        n_outputs: The output number of channels.
        name: The variable scope to provide to W and biases.
        filter_length: The length of the convolution, assumed to be 3.
        rate: The rate or dilation
        batch_size: Non-symbolic value for batch_size.
  
      Returns:
        y: The output of the operation
        (init_1, init_2): Initialization operations for the queues
        (push_1, push_2): Push operations for the queues
      """
      assert filter_length == 3
  
      # create queue
      q_1 = tf.FIFOQueue(
          rate, dtypes=tf.float32, shapes=(batch_size, 1, n_inputs)
      )
      q_2 = tf.FIFOQueue(
          rate, dtypes=tf.float32, shapes=(batch_size, 1, n_inputs)
      )
      init_1 = q_1.enqueue_many(
          tf.zeros((rate, batch_size, 1, n_inputs))
      )
      init_2 = q_2.enqueue_many(
          tf.zeros((rate, batch_size, 1, n_inputs))
      )
      state_1 = q_1.dequeue()
      push_1 = q_1.enqueue(x)
      state_2 = q_2.dequeue()
      push_2 = q_2.enqueue(state_1)
  
      # get pretrained weights
      w = tf.get_variable(
          name=name + "/W",
          shape=[1, filter_length, n_inputs, n_outputs],
          dtype=tf.float32,
      )
      b = tf.get_variable(
          name=name + "/biases", shape=[n_outputs], dtype=tf.float32
      )
      w_q_2 = tf.slice(w, [0, 0, 0, 0], [-1, 1, -1, -1])
      w_q_1 = tf.slice(w, [0, 1, 0, 0], [-1, 1, -1, -1])
      w_x = tf.slice(w, [0, 2, 0, 0], [-1, 1, -1, -1])
  
      # perform op w/ cached states
      y = tf.nn.bias_add(
          tf.matmul(state_2[:, 0, :], w_q_2[0][0])
          + tf.matmul(state_1[:, 0, :], w_q_1[0][0])
          + tf.matmul(x[:, 0, :], w_x[0][0]),
          b,
      )
  
      y = tf.expand_dims(y, 1)
      return y, (init_1, init_2), (push_1, push_2)
  
imports: import importlib
  import os
  import librosa
  import numpy
  import tensorflow.compat.v1
  import tf_slim
label_desc: Use image node_id 28 for calling a global function with example usage: causal_linear(x, n_inputs, n_outputs, name, filter_length, rate, batch_size) and returns: y,  init_1,  init_2,  push_1,  push_2