code: def linear(x, n_inputs, n_outputs, name):
      """Simple linear layer.
  
      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.
  
      Returns:
        y: The output of the operation.
      """
      w = tf.get_variable(
          name=name + "/W",
          shape=[1, 1, n_inputs, n_outputs],
          dtype=tf.float32,
      )
      b = tf.get_variable(
          name=name + "/biases", shape=[n_outputs], dtype=tf.float32
      )
      y = tf.nn.bias_add(tf.matmul(x[:, 0, :], w[0][0]), b)
      y = tf.expand_dims(y, 1)
      return y
  
imports: import importlib
  import os
  import librosa
  import numpy
  import tensorflow.compat.v1
  import tf_slim
label_desc: Use image node_id 29 for calling a global function with example usage: linear(x, n_inputs, n_outputs, name) and returns: y